WARNING:ragas.testset.docstore:Filename and doc_id are the same for all nodes.
Generating: 100%
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[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Synthetic training data', 'Model collapse', 'Environmental impact', 'GAI systems', 'Carbon capture programs']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Equitable design', 'Automated systems', 'Legal protections', 'Proactive equity assessments']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Ongoing monitoring', 'Clear organizational oversight', 'High-quality data', 'Governance procedures']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Automated systems', 'Bias testing', 'Equitable design', 'Systemic biases']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Sensitive domains', 'Enhanced data protections', 'Automated systems', 'Historical discrimination']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Predictive policing system', 'Gun violence risk assessment', 'Watch list transparency', 'System flaws in benefit allocation', 'Lack of explanation for decisions']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data Privacy', 'Privacy Act of 1974', 'NIST Privacy Framework', 'Biometric identifying technology', 'Workplace surveillance']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Sensitive data', 'Sensitive domains', 'Surveillance technology', 'Underserved communities']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Technical protections', 'Rights of the American public', 'Implementation of principles']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Automated systems', 'Timely human consideration', 'Fallback and escalation process', 'Sensitive domains']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Risk assessment', 'Explanatory mechanisms', 'Transparency in decision-making', 'Summary reporting']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Automated systems', 'Bias testing', 'Equitable design', 'Systemic biases']
[ragas.testset.evolutions.INFO] seed question generated: "What role do legal protections play in addressing algorithmic discrimination?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to assess the environmental impact of AI model training and management activities?"
[ragas.testset.evolutions.INFO] seed question generated: "What role has historical discrimination played in the need for enhanced data protections in sensitive domains?"
[ragas.testset.evolutions.INFO] seed question generated: "What actions were taken by the New York state legislature regarding biometric identifying technology in schools?"
[ragas.testset.evolutions.INFO] seed question generated: "What should be included in the governance procedures for the development or use of automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential consequences of using automated systems without protections against algorithmic discrimination?"
[ragas.testset.evolutions.INFO] seed question generated: "What should be included in the summary reporting for automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of transparency in the context of watch lists used by predictive policing systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures are proposed in the Blueprint for an AI Bill of Rights to protect the rights of the American public?"
[ragas.testset.evolutions.INFO] seed question generated: "What does the term 'underserved communities' refer to in the context of the AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential consequences of using automated systems without protections against algorithmic discrimination?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'User consent', 'Automated systems', 'Surveillance technologies', 'Sensitive domains']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic and data-driven harms', 'AI Bill of Rights', 'Panel discussions', 'Consumer rights and protections', 'Automated systems']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Algorithmic discrimination', 'Equity assessments', 'Representative data', 'Proactive testing']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Sensitive domains', 'Enhanced data protections', 'Automated systems', 'Historical discrimination']
[ragas.testset.evolutions.INFO] seed question generated: "What are the reasons for implementing enhanced data protections in sensitive domains?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to ensure that surveillance technologies do not infringe on privacy and civil liberties?"
[ragas.testset.evolutions.INFO] seed question generated: "What was the purpose of the panel discussions organized by the OSTP in relation to the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of proactive testing in the context of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for using automated systems in sensitive domains. It is clear in its intent, seeking information on factors to consider, and does not rely on external references or unspecified contexts. The question is specific enough to be understood and answered by someone with domain knowledge in automated systems and sensitive domains.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the actions taken by the New York state legislature regarding biometric identifying technology in schools. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge and clearly seeks information about legislative actions in a specific context.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the elements that should be included in governance procedures for the development or use of automated systems. It is clear in its intent, seeking specific information about governance procedures, and does not rely on external references or unspecified contexts. The question is self-contained and understandable, making it answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What should be included in the governance procedures for the development or use of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of historical discrimination in the need for enhanced data protections in sensitive domains. It is clear in its intent, seeking an explanation of the connection between past discrimination and current data protection needs. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role has historical discrimination played in the need for enhanced data protections in sensitive domains?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the definition of 'underserved communities' specifically within the context of the AI Bill of Rights. It is clear in its intent, seeking a specific explanation of a term within a defined context. The question is self-contained and does not rely on external references or prior knowledge beyond what is provided in the question itself. Therefore, it meets the criteria for clarity and answerability.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of legal protections in addressing algorithmic discrimination. It is clear in specifying the topic of interest (legal protections and algorithmic discrimination) and seeks information on the impact or function of these protections. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to assess the environmental impact of AI model training and management activities. It is clear in specifying the topic of interest (environmental impact) and the context (AI model training and management activities). The intent is straightforward, seeking specific measures or methods for assessment. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the measures proposed in the 'Blueprint for an AI Bill of Rights' to protect the rights of the American public. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information about the proposed measures in the specified document.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential consequences of using automated systems without protections against algorithmic discrimination. It is clear in its intent, seeking information on the outcomes or risks associated with the lack of safeguards against bias in automated systems. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the potential consequences of using automated systems without protections against algorithmic discrimination?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential consequences of using automated systems without protections against algorithmic discrimination. It is clear in its intent, seeking information on the outcomes or impacts of a specific scenario (lack of protections against algorithmic discrimination in automated systems). The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of transparency in the context of watch lists used by predictive policing systems. It is clear in specifying the topic of interest (transparency) and the specific context (watch lists in predictive policing systems). The intent is to understand the significance of transparency within this specific application, making it understandable and answerable based on the details provided. No additional context or external references are needed to comprehend or respond to the question.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what should be included in the summary reporting for automated systems. While it is clear in its intent to seek information on the components of summary reporting, it is somewhat broad and could benefit from specifying the type of automated systems or the context in which the summary reporting is being used (e.g., performance metrics, error rates, user interactions). Providing more detail would help narrow down the scope and make the question more specific and answerable.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What should be included in the summary reporting for automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the purpose of the panel discussions organized by the OSTP in relation to the Blueprint for an AI Bill of Rights. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of the panel discussions.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What was the purpose of the panel discussions organized by the OSTP in relation to the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure that surveillance technologies do not infringe on privacy and civil liberties. It is clear in its intent, seeking specific actions or strategies to address the potential conflict between surveillance and individual rights. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of proactive testing within the context of automated systems. It is clear in specifying the topic of interest (proactive testing) and the context (automated systems), making the intent straightforward. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of proactive testing in the context of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the reasons behind implementing enhanced data protections in sensitive domains. It is clear in its intent, seeking an explanation for the rationale behind such measures. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. However, it could be improved by specifying what is meant by 'sensitive domains' (e.g., healthcare, finance) to provide more context and focus for the answer.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The term 'underserved communities' refers to communities that have been systematically denied a full opportunity to participate in aspects of economic, social, and civic life.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'When using automated systems in sensitive domains, considerations should include tailoring the systems to their intended purpose, providing meaningful access for oversight, ensuring training for individuals interacting with the system, and incorporating human consideration for adverse or high-risk decisions. Additionally, there should be a focus on accessibility, equity, effectiveness, and the maintenance of these systems, along with public reporting on human governance processes and their outcomes.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key elements that ensure effective governance in the development and use of automated systems. It is clear in its intent, seeking specific information about governance elements, and does not rely on external references or unspecified contexts. The question is self-contained and understandable, making it answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The New York state legislature banned the use of facial recognition systems and other biometric identifying technology in schools until July 1, 2022. Additionally, the law requires that a report on the privacy, civil rights, and civil liberties implications of the use of such technologies be issued before biometric identification technologies can be used in New York schools.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key elements ensure effective governance in automated system development and use?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['OSTP', 'Artificial intelligence', 'Biometric technologies', 'Request For Information (RFI)', 'Public comments']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Transparency is important in the context of watch lists used by predictive policing systems because both police and the public deserve to understand why and how the system makes its determinations. Without transparency, individuals may be placed on a watch list without explanation, leading to a lack of accountability and understanding of the system's conclusions.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The Blueprint for an AI Bill of Rights proposes a set of five principles and associated practices to guide the design, use, and deployment of automated systems to protect the rights of the American public. It includes expectations for automated systems, practical steps for implementation, and emphasizes transparency through reporting to ensure that rights, opportunities, and access are respected.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Technical protections', 'Rights of the American public', 'Implementation of principles']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The potential consequences of using automated systems without protections against algorithmic discrimination include inequitable outcomes, wrongful and discriminatory arrests due to facial recognition technology, discriminatory hiring decisions informed by biased algorithms, and healthcare algorithms that may discount the severity of diseases in certain racial groups. These issues can lead to systemic biases being amplified and harm to underserved communities.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Enhanced data protections in sensitive domains are implemented due to the intimate nature of these domains, the inability of individuals to opt out meaningfully, and the historical discrimination that has often accompanied data knowledge. Additionally, the protections afforded by current legal guidelines may be inadequate given the misuse of tracking technologies and the extensive data footprints individuals leave behind. The American public deserves assurances that data related to sensitive domains is protected and used appropriately, only in narrowly defined contexts with clear benefits to individuals and society.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for proactive steps to ensure that automated systems avoid algorithmic discrimination and promote equity. It is clear in its intent, specifying the desired outcome (avoiding discrimination and promoting equity) and the context (automated systems). The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the outcomes of automated systems that lack safeguards against bias in the contexts of hiring and justice. It is clear in its intent, seeking information on the consequences of such a lack of safeguards. The question is specific and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the main principles outlined in the AI Bill of Rights and how do they aim to protect the rights of the American public?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does artificial intelligence play in the governance and use of biometric technologies according to the OSTP's Request For Information?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Surveillance technologies should be subject to heightened oversight that includes at least pre-deployment assessment of their potential harms and scope limits to protect privacy and civil liberties. Continuous surveillance and monitoring should not be used in education, work, housing, or in other contexts where the use of such surveillance technologies is likely to limit rights, opportunities, or access.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions that algorithmic discrimination may violate legal protections depending on specific circumstances, indicating that legal protections play a role in addressing algorithmic discrimination.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Surveillance and data collection', 'Consumer data protection', 'Automated systems', 'Mental health impacts']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What proactive steps ensure automated systems avoid algorithmic discrimination and promote equity?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What outcomes arise from automated systems lacking safeguards against bias in hiring and justice?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks for what should be included in governance procedures, implying a detailed list or framework. The second question is broader, asking what ensures good governance, which could include principles, practices, or outcomes. Thus, they differ in depth and breadth.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the insights that the Office of Science and Technology Policy (OSTP) aimed to gather from diverse experts during panel discussions for the AI Bill of Rights. It is clear in specifying the context (OSTP, AI Bill of Rights) and the type of information sought (insights from panel discussions). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The suggested measures to assess the environmental impact of AI model training and management activities include: 1) Assessing safety to physical environments when deploying GAI systems, 2) Documenting anticipated environmental impacts of model development, maintenance, and deployment in product design decisions, 3) Measuring or estimating environmental impacts such as energy and water consumption for training, fine-tuning, and deploying models, and verifying trade-offs between resources used at inference time versus additional resources required at training time, and 4) Verifying the effectiveness of carbon capture or offset programs for GAI training and applications, while addressing green-washing concerns.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the mental health impacts associated with increased use of surveillance technologies in schools and workplaces?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Disinformation and misinformation', 'Generative AI models', 'Information security risks', 'Cybersecurity attacks']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 3, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Algorithmic discrimination protections', 'Equitable design', 'Independent evaluation and reporting']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What insights did OSTP aim to gather from diverse experts during panel discussions for the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Reporting expectations', 'Transparency', 'Artificial Intelligence ethics', 'Traffic calming measures', 'AI Risk Management Framework']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Sensitive domains', 'Predictive analytics', 'Student data collection', 'Employee data transfer']
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with generative AI models in the context of disinformation and cybersecurity?"
[ragas.testset.evolutions.INFO] seed question generated: "What protections does the AI Bill of Rights provide against algorithmic discrimination?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'AI Bill of Rights', 'Civil rights and liberties', 'Equal opportunities', 'Access to critical resources']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of proactive testing in automated systems, while the second question addresses methods to prevent algorithmic bias. These are distinct topics with different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Technical protections', 'Rights of the American public', 'Implementation of principles']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question addresses the broader consequences of using automated systems without protections against algorithmic discrimination, while the second question focuses specifically on issues in hiring and justice, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about historical factors that necessitate extra data protections in sensitive domains like health and finance. It is clear in specifying the domains of interest (health and finance) and the type of information sought (historical factors necessitating extra data protections). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are some concerns related to data privacy in the context of sensitive domains?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Generative AI Public Working Group', 'GAI risk management', 'Governance', 'Content Provenance', 'AI lifecycle risks']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the main principles outlined in the AI Bill of Rights and how they aim to protect the rights of the American public. It is clear in specifying the document of interest (AI Bill of Rights) and seeks detailed information on both the principles and their protective measures. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the main principles outlined in the AI Bill of Rights and how do they aim to protect the rights of the American public?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do technical protections play in the implementation of the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of automated systems that should be covered by the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What historical factors necessitate extra data protections in sensitive domains like health and finance?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the different stages of the AI lifecycle where risks can arise?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of the panel discussions, while the second question seeks specific insights from experts. These inquiries have different depths and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of artificial intelligence in the governance and use of biometric technologies according to the OSTP's Request For Information. While it specifies the topic (AI's role in biometric technologies) and the source (OSTP's Request For Information), it assumes familiarity with the specific document without providing its content or context. This makes the question unclear for those who do not have access to or knowledge of the OSTP's Request For Information. To improve clarity and answerability, the question could include a brief summary or key points from the OSTP's document, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What role does artificial intelligence play in the governance and use of biometric technologies according to the OSTP's Request For Information?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the mental health impacts associated with the increased use of surveillance technologies in schools and workplaces. It is clear in specifying the context (schools and workplaces) and the focus (mental health impacts), making the intent straightforward. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what should be included in the summary reporting for automated systems. It is clear in its intent, seeking specific information about the components or elements that should be part of such a summary report. The question is independent and does not rely on external references or additional context to be understood. However, it could be improved by specifying the type of automated systems (e.g., software, industrial automation) to provide more precise guidance.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Good governance in automated systems is ensured by laying out clear governance structures and procedures, which include clearly-stated governance procedures before deploying the system, as well as the responsibility of specific individuals or entities to oversee ongoing assessment and mitigation. Organizational stakeholders should be involved in establishing these governance procedures, and responsibility should rest high enough in the organization to allow for prompt decision-making regarding resources, mitigation, incident response, and potential rollback. Additionally, those in charge should be aware of any use cases with the potential for meaningful impact on people's rights, opportunities, or access, and it may be appropriate for an independent ethics review to be conducted before deployment.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the potential risks associated with generative AI models in the context of disinformation and cybersecurity. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on risks related to two specific areas: disinformation and cybersecurity.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the potential risks associated with generative AI models in the context of disinformation and cybersecurity?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the protections provided by the AI Bill of Rights against algorithmic discrimination. It is specific in its focus on the AI Bill of Rights and the particular issue of algorithmic discrimination. The intent is clear, seeking information on the protections offered. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of the AI Bill of Rights and algorithmic discrimination.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology (NIST). It is specific, independent, and has a clear intent, seeking information about the purpose of a particular framework from a specific organization. The question does not rely on external references or additional context beyond what is provided within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically focuses on the impact of historical discrimination on the need for enhanced data protections, while the second question broadly asks about the drivers of extra data protections in health and finance without mentioning historical discrimination. This leads to different depths and focuses of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about concerns related to data privacy in sensitive domains. It is clear in its intent, seeking information on potential issues or challenges in this area. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. However, it could be improved by specifying what is meant by 'sensitive domains' (e.g., healthcare, finance) to narrow down the scope and provide a more focused answer.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The mental health impacts associated with increased use of surveillance technologies in schools and workplaces include lowered self-confidence, anxiety, depression, and a reduced ability to use analytical reasoning.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of technical protections in the implementation of the Blueprint for an AI Bill of Rights. It is clear in specifying the topic of interest (technical protections) and the context (Blueprint for an AI Bill of Rights). The intent is to understand the specific contributions or functions of technical protections within this framework. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for examples of automated systems that should be covered by the Blueprint for an AI Bill of Rights. It is clear in its intent, seeking specific examples of automated systems relevant to the AI Bill of Rights. The question is independent and does not rely on external references or prior knowledge beyond a general understanding of automated systems and the concept of an AI Bill of Rights. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the five principles of the AI Bill of Rights and how they ensure public protection. It is specific, independent, and has a clear intent. The question does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Trustworthy AI', 'Transparency policies', 'Risk management activities', 'Information integrity', 'GAI capabilities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the different stages of the AI lifecycle where risks can arise. It is clear in specifying the topic of interest (stages of the AI lifecycle) and seeks detailed information on potential risks at each stage. The question is self-contained and does not rely on external references or prior knowledge not shared within the question itself. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Biased automated systems in hiring can lead to discriminatory decisions, such as hiring tools that reject women applicants for spurious reasons, penalizing resumes with the word 'women’s'. In the justice system, predictive models can disproportionately label Black students as high risk of dropping out, and risk assessment tools can overpredict recidivism for some groups of color, leading to unfair treatment and outcomes.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To prevent algorithmic bias in automated systems, proactive equity assessments should be conducted during the design phase to identify potential discrimination and effects on equity. Data used in system development should be representative and reviewed for bias, and the use of demographic information should be avoided to prevent algorithmic discrimination. Proactive testing should be performed to identify and remove proxies that may lead to discrimination, and organizations should monitor systems closely for any resulting algorithmic discrimination.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'OSTP sought insights and analysis on the risks, harms, benefits, and policy opportunities of automated systems from a variety of experts, practitioners, advocates, and federal government officials during the AI Bill of Rights panels. The discussions focused on consumer rights and protections, the criminal justice system, equal opportunities and civil justice, artificial intelligence and democratic values, social welfare and development, and the healthcare system.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What are the five principles of the AI Bill of Rights and how do they ensure public protection?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of artificial intelligence in the governance and use of biometric technologies according to the OSTP's Request For Information. While it specifies the context (OSTP's Request For Information), it assumes the reader has access to or knowledge of this specific document, which is not provided within the question itself. To improve clarity and answerability, the question could include a brief summary or key points from the OSTP's Request For Information relevant to AI and biometric technologies, or it could be rephrased to ask about general trends or findings in this area without relying on a specific document.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What factors should be considered to ensure information integrity in the context of GAI risk management?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Incident response plans', 'Third-party GAI technologies', 'Data privacy', 'Continuous monitoring', 'Vendor contracts']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The summary reporting for automated systems should include: the responsible entities for accountability purposes; the goal and use cases for the system; identified users and impacted populations; the assessment of notice clarity and timeliness; the assessment of the explanation's validity and accessibility; the assessment of the level of risk; and the account and assessment of how explanations are tailored, including to the purpose, the recipient of the explanation, and the level of risk.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The AI Bill of Rights provides protections against algorithmic discrimination by ensuring that individuals should not face discrimination by algorithms. It mandates that systems should be designed and used in an equitable way, taking proactive and continuous measures to protect individuals and communities from algorithmic discrimination. This includes conducting proactive equity assessments, using representative data, ensuring accessibility for people with disabilities, performing pre-deployment and ongoing disparity testing, and providing clear organizational oversight. Additionally, independent evaluation and reporting, including algorithmic impact assessments and disparity testing results, should be made public whenever possible to confirm these protections.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Extra data protections in health and finance are driven by the intimate nature of these domains, the inability of individuals to opt out in a meaningful way, and the historical discrimination that has often accompanied data knowledge. Additionally, the potential for material harms, including significant adverse effects on human rights such as autonomy and dignity, civil liberties, and civil rights, necessitates enhanced protections.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Technical protections and practices laid out in the Blueprint for an AI Bill of Rights help guard the American public against many potential and actual harms associated with automated systems. They provide a framework for the design, use, and deployment of these systems to protect the rights of individuals, ensuring transparency and accountability in their operation.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of regularly assessing and verifying security measures in information security?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Risks can arise during the design, development, deployment, operation, and/or decommissioning stages of the AI lifecycle.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the principles of the AI Bill of Rights and their role in protecting the public, requiring similar depth and breadth of information.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when reviewing vendor contracts for third-party GAI technologies?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connection between misinformation risks from GAI (General Artificial Intelligence) and cybersecurity threats in malicious contexts. While it is clear in its intent to explore the relationship between these two areas, it lacks sufficient context to be fully self-contained. The term 'GAI' might not be universally understood without further explanation, and 'malicious contexts' could be interpreted in various ways. To improve clarity and answerability, the question could specify what is meant by 'GAI' and provide examples or a brief description of the 'malicious contexts' being referred to.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What links misinformation risks from GAI to cybersecurity threats in malicious contexts?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to ensure effective human-AI configuration in the context of GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI risks management', 'Risk response options', 'Model release approaches', 'Information security', 'Harmful bias mitigation']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key aspects that ensure transparency in AI systems according to the NIST framework. It is specific in its focus on transparency and the NIST framework, making the intent clear. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role do automated systems play in the protection of civil rights and democratic values according to the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Examples of automated systems that should be covered by the Blueprint for an AI Bill of Rights include: speech-related systems such as automated content moderation tools; surveillance and criminal justice system algorithms like risk assessments and predictive policing; voting-related systems such as signature matching tools; privacy-impacting systems like smart home systems and health-related data systems; education-related systems such as algorithms for detecting student cheating; housing-related systems like tenant screening algorithms; and employment-related systems that inform terms of employment.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key aspects ensure transparency in AI systems as per the NIST framework?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Concerns related to data privacy in sensitive domains include the lack of awareness among patients regarding the use of their medical data by insurance companies, the revelation of personal information (such as pregnancy) through targeted advertising, the monitoring of student conversations which may limit emotional expression and unfairly flag students with disabilities, the use of location data to identify individuals visiting abortion clinics, the collection of sensitive student data without parental consent, and the potential for discriminatory impacts from such data usage. Additionally, there are concerns about the accuracy of employee data transferred to third parties, which can affect job opportunities.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What techniques can be employed to mitigate harmful bias in AI-generated content?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about factors to consider for ensuring information integrity in the context of GAI (General Artificial Intelligence) risk management. It is clear in its intent, specifying the topic (information integrity) and the context (GAI risk management). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated system', 'Plain language documentation', 'System functioning', 'Outcome explanations', 'User notification']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI trustworthy characteristics', 'Human-AI Configuration', 'Information Integrity', 'Data Privacy', 'Confabulation']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Participatory engagement methods', 'Field testing', 'AI red-teaming', 'User feedback', 'Risk management']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Lisa Feldman Barrett', 'Microsoft Corporation', 'National Association for the Advancement of Colored People', 'University of Michigan Ann Arbor', 'OSTP listening sessions']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human subject protection', 'Content provenance', 'Data privacy', 'AI system performance', 'Anonymization techniques']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.evolutions.INFO] seed question generated: "What methods can organizations use to collect user feedback during product development?"
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account regarding data privacy when deploying a GAI system?"
[ragas.testset.evolutions.INFO] seed question generated: "What should designers and developers provide to ensure clear understanding of system functioning in automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of using digital content transparency solutions in AI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of regularly assessing and verifying security measures in information security. It is clear in its intent, seeking an explanation of the reasons behind these practices. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of regularly assessing and verifying security measures in information security?"
[ragas.testset.evolutions.INFO] seed question generated: "What program is associated with the University of Michigan Ann Arbor mentioned in the context?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'AI Actors', 'Unanticipated impacts', 'Information integrity', 'Content provenance']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for considerations when reviewing vendor contracts for third-party GAI (General Artificial Intelligence) technologies. It is clear in its intent, specifying the context (vendor contracts) and the subject matter (third-party GAI technologies). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What criteria are used to measure AI system performance or assurance in deployment settings?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure effective human-AI configuration in the context of GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (human-AI configuration) and the context (GAI systems), making the intent clear. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the overall purpose of the AI Risk Management Framework by NIST, while the second question specifically focuses on AI transparency as per NIST. These questions have different constraints and requirements, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to ensure information integrity in the context of AI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for automated systems regarding safety and effectiveness?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of automated systems in protecting civil rights and democratic values as outlined in the 'Blueprint for an AI Bill of Rights'. It is specific in its focus on automated systems and their role, and it clearly references a particular document (the Blueprint for an AI Bill of Rights). The intent is clear, seeking an explanation or summary of the role described in the specified document. The question is self-contained and does not rely on external references beyond the mentioned document, making it understandable and answerable given sufficient domain knowledge.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role do automated systems play in the protection of civil rights and democratic values according to the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for techniques to mitigate harmful bias in AI-generated content. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge and clearly seeks information on methods to address bias in AI content generation.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What techniques can be employed to mitigate harmful bias in AI-generated content?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connection between misinformation risks from GAI (General Artificial Intelligence) and cybersecurity threats in malicious contexts. While it specifies the two areas of interest (misinformation risks from GAI and cybersecurity threats), it is somewhat vague in its phrasing. The term 'malicious contexts' is broad and could benefit from further specification. Additionally, the question could be clearer by defining what is meant by 'links' (e.g., mechanisms, examples, impacts). To improve clarity and answerability, the question could be reframed to specify the type of links or mechanisms being inquired about and provide more context on what is meant by 'malicious contexts'.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Factors to consider to ensure information integrity in the context of GAI risk management include abuses and impacts to information integrity, dependencies between GAI and other IT or data systems, harm to fundamental rights or public safety, presentation of obscene, objectionable, offensive, discriminatory, invalid or untruthful output, psychological impacts to humans, possibility for malicious use, introduction of significant new security vulnerabilities, anticipated system impact on some groups compared to others, and unreliable decision-making capabilities.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for methods that organizations can use to collect user feedback during product development. It does not rely on external references or prior knowledge and has a clear intent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What methods can organizations use to collect user feedback during product development?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what designers and developers should provide to ensure a clear understanding of system functioning in automated systems. It is specific and independent, as it does not rely on external references or prior knowledge. The intent is clear, seeking information on best practices or necessary elements for clarity in automated systems. No improvements are necessary.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for data privacy when deploying a GAI (General Artificial Intelligence) system. It is clear in its intent, seeking specific information on data privacy aspects related to GAI deployment. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What considerations should be taken into account regarding data privacy when deploying a GAI system?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of using digital content transparency solutions in AI systems. It is clear in specifying the topic of interest (digital content transparency solutions in AI systems) and seeks information on the purpose or rationale behind their use. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of using digital content transparency solutions in AI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about a program associated with the University of Michigan Ann Arbor mentioned in an unspecified context. It is unclear because it refers to 'the context' without providing any details or description of what this context entails. This makes the question dependent on external information that is not included within the query itself. To improve clarity and answerability, the question should specify the context or provide more details about the program of interest.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What program is associated with the University of Michigan Ann Arbor mentioned in the context?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The suggested measures to ensure effective human-AI configuration in the context of GAI systems include documenting the instructions given to data annotators or AI red-teamers (MS-2.8-002) and verifying the adequacy of GAI system user instructions through user testing (MS-2.8-004).', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the criteria used to measure AI system performance or assurance in deployment settings. It is clear in specifying the topic of interest (criteria for measuring AI system performance or assurance) and the context (deployment settings). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What criteria are used to measure AI system performance or assurance in deployment settings?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Algorithmic discrimination', 'Equity assessments', 'Representative data', 'Proactive testing']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What guidelines does the Blueprint for an AI Bill of Rights propose to ensure that automated systems uphold civil rights and democratic principles in the face of technological challenges?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the connections between assessing security measures and ensuring information integrity. It is clear in its intent, seeking to understand the relationship between two specific concepts: security measures and information integrity. The question is self-contained and does not rely on external references or additional context to be understood. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for automated systems in terms of safety and effectiveness. It is clear in its intent, seeking information on the standards or criteria that automated systems should meet regarding these two aspects. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the expectations for automated systems regarding safety and effectiveness?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of proactive testing in the context of automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What diverse strategies can organizations employ to gather user insights during the early stages of product development while ensuring compliance with ethical standards?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Policies and procedures for human-AI configurations', 'Oversight of GAI systems', 'Risk measurement processes', 'Human-AI configuration', 'Threat modeling for GAI systems']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between assessing security measures and ensuring information integrity?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Designers, developers, and deployers of automated systems should provide generally accessible plain language documentation that includes clear descriptions of the overall system functioning and the role automation plays.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'When reviewing vendor contracts for third-party GAI technologies, considerations should include avoiding arbitrary or capricious termination of critical GAI technologies or vendor services, avoiding non-standard terms that may amplify or defer liability in unexpected ways, and preventing unauthorized data collection by vendors or third-parties. Additionally, there should be a clear assignment of liability and responsibility for incidents, acknowledgment of GAI system changes over time, and requirements for notification and disclosure for serious incidents arising from third-party data and systems. Service Level Agreements (SLAs) in vendor contracts should also address incident response, response times, and availability of critical support.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What multifaceted factors regarding data privacy and content integrity must be evaluated when implementing a GAI system, particularly in relation to user feedback and the system's operational transparency?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI lifecycle', 'AI technology risks', 'Organizational practices for AI', 'Impact documentation process', 'Content provenance methodologies']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of engaging in threat modeling for GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for methods to address harmful bias in AI outputs while ensuring content integrity. It is clear in its intent, specifying the problem (harmful bias) and the desired outcome (ensuring content integrity). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about a program associated with the University of Michigan Ann Arbor, but it references 'the context' without providing any specific context within the question itself. This makes the question unclear and dependent on external information that is not included. To improve clarity and answerability, the question should specify the context or provide more details about the program of interest.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the impact documentation process in the context of GAI systems?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What methods can be used to address harmful bias in AI outputs while ensuring content integrity?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What criteria should automated systems meet to ensure both safety and the prevention of algorithmic discrimination, and how should these be independently evaluated and reported?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of digital content transparency solutions in ensuring traceability and integrity in AI. It is clear in specifying the topic of interest (digital content transparency solutions) and the aspects it seeks to address (traceability and integrity in AI). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of proactive testing within the context of automated systems. It is clear in specifying the topic of interest (proactive testing) and the context (automated systems), making the intent straightforward. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of proactive testing in the context of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for diverse strategies that organizations can use to gather user insights during the early stages of product development while ensuring compliance with ethical standards. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking actionable strategies that balance user insight collection with ethical considerations.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What role do digital content transparency solutions play in ensuring traceability and integrity in AI?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of regularly assessing and verifying security measures, while the second question asks about the relationship between security measures and information integrity. These questions have different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for methods that assess AI performance while ensuring human subject protection and data privacy. It is clear in specifying the dual focus on performance assessment and ethical considerations (human subject protection and data privacy). The intent is straightforward, seeking information on methodologies that balance these aspects. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Prompt injection', 'Indirect prompt injection attacks', 'Data poisoning', 'Intellectual property risks', 'Obscene and degrading content']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the issue of bias in AI-generated content and seek methods to mitigate it while maintaining content integrity. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the guidelines proposed by the Blueprint for an AI Bill of Rights to ensure that automated systems uphold civil rights and democratic principles amidst technological challenges. It is specific in its focus on the Blueprint for an AI Bill of Rights and the type of guidelines it seeks (those ensuring civil rights and democratic principles). The intent is clear, and the question is self-contained, not relying on external references or prior knowledge beyond what is mentioned in the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies can orgs use to gather user insights ethically in early product dev?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What methods assess AI performance while ensuring human subject protection and data privacy?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Sensitive data', 'Ethical review', 'Data quality', 'Access limitations']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the multifaceted factors related to data privacy and content integrity that need to be evaluated when implementing a GAI (Generative AI) system, with a particular focus on user feedback and the system's operational transparency. It is clear in specifying the areas of interest (data privacy, content integrity, user feedback, operational transparency) and seeks detailed information on these aspects. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of engaging in threat modeling for GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (threat modeling for GAI systems) and seeks an explanation of the purpose behind this activity. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are indirect prompt injection attacks and how do they exploit vulnerabilities in GAI-integrated applications?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What does the AI Bill of Rights suggest for protecting civil rights in tech?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the impact documentation process specifically in the context of GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (impact documentation process) and the context (GAI systems), making the intent of the question straightforward. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the impact documentation process in the context of GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for the criteria that automated systems should meet to ensure safety and prevent algorithmic discrimination. It also inquires about how these criteria should be independently evaluated and reported. The intent is clear, and the question is self-contained, not relying on external references or prior knowledge. It is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures should entities take to maintain data quality in sensitive domains?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What factors on data privacy and content integrity should be considered for a GAI system, especially regarding user feedback and transparency?"
[ragas.testset.evolutions.INFO] seed question generated: "What precautions should be taken when using derived data sources in automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure information integrity in the context of AI systems. It is clear in specifying the topic of interest (information integrity) and the context (AI systems), making the intent clear and understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it independent and answerable based on domain knowledge.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What measures are suggested to ensure information integrity in the context of AI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the purpose of using digital content transparency solutions in AI systems, while the second question focuses on how these tools ensure AI traceability and integrity. These questions have different requirements and depths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What proactive measures are essential in the design and evaluation of automated systems to ensure they effectively mitigate algorithmic discrimination and promote equity?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions focus on methods or strategies for collecting user feedback or insights during product development. They share similar constraints and requirements, as well as the same depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology in social welfare', 'Fraud detection', 'Digital ID systems', 'Healthcare access and delivery', 'Health disparities']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What standards should automated systems follow for safety and fairness, and how to assess them?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the criteria used to measure AI system performance or assurance in deployment settings, which is broader. The second question specifically targets AI performance evaluation with a focus on human safety and privacy, leading to different depths and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.evolutions.INFO] seed question generated: "What concerns were raised by panelists regarding healthcare access and delivery in relation to new technologies?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Engaging in threat modeling for GAI systems is intended to anticipate potential risks from these systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address data privacy in the context of deploying a GAI system, the second question also includes considerations for content integrity, user feedback, and transparency, leading to a broader scope and different depth of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does the documentation of risks and impacts play in ensuring compliance and effective governance throughout the lifecycle of GAI systems, particularly in relation to external feedback mechanisms?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do healthcare navigators play in helping consumers find health coverage options?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question specifically asks about the role of automated systems in the protection of civil rights and democratic values according to the Blueprint for an AI Bill of Rights, requiring a detailed explanation. The second question is broader, asking generally about the AI Bill of Rights' suggestions for protecting civil rights in tech, which may not necessarily focus on automated systems or democratic values.", 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures that entities should take to maintain data quality in sensitive domains. It is clear in its intent, seeking specific actions or strategies for ensuring data quality. The question is independent and does not rely on external references or unspecified contexts. It is specific enough to be understood and answered by someone with domain knowledge in data quality management or sensitive data handling.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for precautions to be taken when using derived data sources in automated systems. It is clear in specifying the topic of interest (precautions, derived data sources, automated systems) and seeks detailed information on safety or best practices. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What precautions should be taken when using derived data sources in automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for proactive measures in the design and evaluation of automated systems to mitigate algorithmic discrimination and promote equity. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on essential measures for addressing algorithmic discrimination and promoting equity in automated systems.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are some real-life examples of how human alternatives can be implemented in practice?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated traffic control systems', 'Smart city technologies', 'Fraud detection algorithms', 'Biometric systems', 'Access control algorithms']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps ensure automated systems reduce bias and promote equity?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Information sharing and feedback mechanisms', 'AI impact assessment', 'Organizational policies', 'Third-party rights']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Digital content transparency solutions ensure AI traceability and integrity by enabling the documentation of each instance where content is generated, modified, or shared, providing a tamper-proof history of the content. Additionally, robust version control systems can be applied to track changes across the AI lifecycle over time.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'AI performance is evaluated with human safety and privacy in mind by implementing measures such as assessing and managing statistical biases related to GAI content provenance, documenting how content provenance data is tracked, providing human subjects with options to withdraw participation or revoke consent, and using techniques like anonymization and differential privacy to minimize risks associated with linking AI-generated content back to individual human subjects.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role do biometric systems play in access control?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the role of healthcare navigators in assisting consumers with finding health coverage options. It does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. The intent is clear, seeking information on the functions and contributions of healthcare navigators in the context of health coverage.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role do healthcare navigators play in helping consumers find health coverage options?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the actions that connect AI Actor engagement to measures ensuring content authenticity and integrity. While it is clear in its intent to understand the relationship between AI Actor engagement and content authenticity measures, it is somewhat vague in defining what is meant by 'AI Actor engagement' and the specific 'measures' being referred to. To improve clarity and answerability, the question could specify what is meant by 'AI Actor engagement' (e.g., specific activities or roles of AI systems) and provide examples or types of 'measures' for content authenticity and integrity.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What actions link AI Actor engagement to measures ensuring content authenticity and integrity?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about concerns raised by panelists regarding healthcare access and delivery in relation to new technologies. It is clear in specifying the topic of interest (concerns, healthcare access and delivery, new technologies) and seeks detailed information on the concerns raised. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What concerns were raised by panelists regarding healthcare access and delivery in relation to new technologies?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of information sharing and feedback mechanisms in relation to GAI systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What measures should be implemented to ensure the safe use of derived data in automated systems while preventing algorithmic discrimination?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The AI Bill of Rights suggests guiding the design, use, and deployment of automated systems to protect the American public, ensuring that these technologies reinforce civil rights and democratic values. It emphasizes the need to root out inequity, embed fairness in decision-making processes, and affirmatively advance civil rights, equal opportunity, and racial justice in America.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Entities should be especially careful to maintain the quality of data in sensitive domains to avoid adverse consequences arising from decision-making based on flawed or inaccurate data. This includes conducting regular, independent audits and taking prompt corrective measures to maintain accurate, timely, and complete data.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Surveillance and data collection', 'Consumer data protection', 'Automated systems', 'Mental health impacts']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Generative AI Public Working Group', 'GAI risk management', 'Governance', 'Content Provenance', 'AI lifecycle risks']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of proactive testing in automated systems, while the second question is about steps to reduce bias and promote equity in automated systems. These questions have different constraints and requirements, as well as different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Factors on data privacy and content integrity for a GAI system include documenting the extent to which human domain knowledge is employed to improve GAI system performance, reviewing and verifying sources and citations in GAI system outputs, tracking instances of anthropomorphization in GAI system interfaces, verifying GAI system training data and TEVV data provenance, and regularly reviewing security and safety guardrails. Additionally, structured feedback about content provenance should be recorded and integrated from operators, users, and impacted communities, and there should be an emphasis on digital content transparency regarding the societal impacts of AI and the role of diverse and inclusive content generation.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with surveillance and data collection on the American public?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of content provenance in the context of GAI risk management?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of documenting risks and impacts in ensuring compliance and effective governance throughout the lifecycle of GAI (General Artificial Intelligence) systems, with a particular focus on external feedback mechanisms. It is clear in specifying the topic of interest (documentation of risks and impacts, compliance, governance, GAI systems, external feedback mechanisms) and seeks detailed information on the relationship between these elements. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how 'human alternatives' can be implemented in practice. While it is clear that the question seeks practical examples, the term 'human alternatives' is vague and could refer to various concepts such as alternative energy sources, alternative medicine, or even alternative dispute resolution methods. To improve clarity and answerability, the question should specify what is meant by 'human alternatives' and the context in which they are to be implemented (e.g., in technology, healthcare, environmental practices).", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are some real-life examples of how human alternatives can be implemented in practice?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Transparency artifacts', 'Explainable AI (XAI)', 'Pre-trained models', 'Harmful bias', 'Content filters']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of biometric systems in access control. It is clear and specific, seeking information on the function and importance of biometric systems within the context of access control. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role do biometric systems play in access control?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about indirect prompt injection attacks and how they exploit vulnerabilities in GAI-integrated applications. It is specific, independent, and has a clear intent. The question does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does risk documentation aid compliance and governance in GAI systems, especially with external feedback?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of bias and discrimination in automated systems on the rights of the American public?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for measures to ensure the safe use of derived data in automated systems while preventing algorithmic discrimination. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking actionable measures or strategies to address the issue of algorithmic discrimination in the context of automated systems using derived data.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to mitigate risks related to harmful bias in generative AI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Synthetic training data', 'Model collapse', 'Environmental impact', 'GAI systems', 'Carbon capture programs']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the purpose of information sharing and feedback mechanisms in relation to GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (information sharing and feedback mechanisms) and the context (GAI systems). The intent is to understand the purpose of these mechanisms, which is straightforward and unambiguous. The question is self-contained and does not rely on external references or prior knowledge beyond a basic understanding of GAI systems.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of information sharing and feedback mechanisms in relation to GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connections between Navigators' training and their role in aiding health coverage access. It is clear in specifying the topic of interest (Navigators' training and their role in health coverage access) and seeks information on the relationship between these two aspects. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on expectations for safety and effectiveness, while the second question addresses standards for safety and fairness and how to assess them, indicating different requirements and depths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the issues highlighted by panelists regarding technology's role in healthcare access and equity. It is clear in specifying the topic of interest (tech's role in healthcare access and equity) and seeks detailed information on the issues identified by panelists. The intent is clear, and the question is independent as it does not rely on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps ensure safe use of derived data in automated systems, avoiding bias?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between Navigators' training and their role in aiding health coverage access?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of verifying the effectiveness of carbon capture programs in relation to GAI training and applications?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of content provenance in the context of GAI (General Artificial Intelligence) risk management. It is clear in specifying the topic of interest (content provenance) and the context (GAI risk management), making the intent clear and understandable. The question does not rely on external references or unspecified contexts, making it self-contained and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the significance of content provenance in the context of GAI risk management?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential risks associated with surveillance and data collection on the American public. It is clear in specifying the topic of interest (surveillance and data collection) and the population concerned (the American public). The intent is to understand the risks, which is straightforward and unambiguous. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of surveillance and data collection practices.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the potential risks associated with surveillance and data collection on the American public?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of the impact documentation process, while the second question is about how risk documentation aids compliance and governance, particularly with external feedback. These questions have different constraints and requirements, as well as different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Indirect prompt injection attacks occur when adversaries remotely exploit LLM-integrated applications by injecting prompts into data likely to be retrieved. These attacks can exploit vulnerabilities by stealing proprietary data or running malicious code remotely on a machine.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how 'human alternatives' can be implemented in practice. While it is clear that the question seeks practical examples, the term 'human alternatives' is vague and could refer to various concepts such as alternative energy sources, alternative medicine, or even alternative dispute resolution methods. To improve clarity and answerability, the question should specify what is meant by 'human alternatives' and possibly provide a context or domain (e.g., technology, healthcare, environmental science) in which these alternatives are to be considered.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What issues did panelists highlight about tech's role in healthcare access and equity?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What roles do oversight policies and feedback mechanisms play in mitigating risks associated with GAI systems and ensuring effective communication of their societal impacts?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the actions that connect AI Actor engagement to measures ensuring content authenticity and integrity. While it is clear in its intent to understand the relationship between AI Actor engagement and content authenticity measures, it lacks specificity regarding what is meant by 'AI Actor engagement' and the types of 'measures' being referred to. To improve clarity and answerability, the question could specify the context or examples of AI Actor engagement (e.g., content creation, moderation) and the types of measures (e.g., verification processes, blockchain technology) it is interested in.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To ensure automated systems reduce bias and promote equity, several steps should be taken: 1) Conduct proactive equity assessments during the design phase to identify potential discrimination and effects on equity; 2) Use representative and robust data that reflects local communities and is reviewed for bias; 3) Guard against proxies by avoiding the direct use of demographic information in system design and testing for correlations; 4) Allow independent evaluations of potential algorithmic discrimination; 5) Provide reporting of algorithmic impact assessments that detail consultations, equity assessments, and any disparities found, ensuring transparency and public accountability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to mitigate risks related to harmful bias in generative AI systems. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information on suggested measures for a particular issue in generative AI systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of bias and discrimination in automated systems on the rights of the American public. It is specific in its focus on bias and discrimination within automated systems and their impact on rights, and it is clear in its intent to understand the consequences of these issues. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of automated systems and civil rights, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about systems that assist in adjudicating access control and the role of biometrics within these systems. It is clear in its intent, seeking information on both the types of systems used for access control and the specific application of biometrics. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the specific role of healthcare navigators in helping consumers find health coverage options, while the second question is about the relationship between Navigator training and health coverage access. These questions have different constraints and requirements.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems should follow standards that include independent evaluation, regular reporting, and protections against algorithmic discrimination. They should be designed to allow independent evaluators access to assess safety and effectiveness, with regular updates on system performance, data usage, risk management, and independent evaluations. Additionally, entities should conduct algorithmic impact assessments to evaluate potential discrimination and ensure transparency in reporting these assessments.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What multifaceted dangers arise from the intersection of pervasive surveillance practices and the unregulated collection of personal data on the American populace?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does content provenance play in mitigating unique risks associated with GAI, as highlighted by stakeholder consultations?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-generated content', 'Real-time auditing tools', 'User feedback mechanisms', 'Synthetic data', 'Incident response and recovery plans']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not provide specific information on how risk documentation aids compliance and governance in GAI systems, particularly regarding external feedback.', 'verdict': -1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions ask about concerns raised by panelists regarding healthcare access and delivery in relation to new technologies. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What systems assist in adjudicating access control and how do biometrics fit in?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Automated systems', 'Human fallback', 'Critical protections', 'Voting process']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Artificial Intelligence and Democratic Values', 'Non-discriminatory technology', 'Explainable AI', 'Community participation', 'Social welfare systems']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of using structured feedback mechanisms in relation to AI-generated content?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of having a human fallback system in automated processes?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Predictive policing system', 'Gun violence risk assessment', 'Watch list transparency', 'System flaws in benefit allocation', 'Lack of explanation for decisions']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the purpose of verifying the effectiveness of carbon capture programs in relation to GAI (presumably General Artificial Intelligence) training and applications. While it specifies the topic of interest (carbon capture programs and GAI), it is somewhat ambiguous due to the lack of clarity on how these two areas are related. The term 'GAI' is not commonly used and could be confusing without further context. To improve clarity and answerability, the question could benefit from specifying what 'GAI' stands for and explaining the connection between carbon capture programs and AI training or applications. For example, 'What is the purpose of verifying the effectiveness of carbon capture programs in relation to the environmental impact of training General Artificial Intelligence (GAI) models?'", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What is the purpose of verifying the effectiveness of carbon capture programs in relation to GAI training and applications?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key aspects of designing explainable AI as discussed in the panel on Artificial Intelligence and Democratic Values?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the roles of oversight policies and feedback mechanisms in mitigating risks associated with GAI (General Artificial Intelligence) systems and ensuring effective communication of their societal impacts. It is clear in specifying the elements of interest (oversight policies, feedback mechanisms, GAI systems) and the aspects to be addressed (risk mitigation, communication of societal impacts). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-generated content', 'Real-time auditing tools', 'User feedback mechanisms', 'Synthetic data', 'Incident response and recovery plans']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The implications of bias and discrimination in automated systems on the rights of the American public include limiting opportunities, preventing access to critical resources or services, and reflecting or reproducing existing unwanted inequities. These outcomes can threaten people's opportunities, undermine their privacy, and lead to pervasive tracking of their activities, often without their knowledge or consent.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What issues arise from system flaws in benefit allocation?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To mitigate risks related to harmful bias in generative AI systems, the suggested measures include applying explainable AI (XAI) techniques as part of ongoing continuous improvement processes, documenting how pre-trained models have been adapted for specific generative tasks, and documenting sources and types of training data along with potential biases present in the data.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Navigator training is related to health coverage access as it equips individuals or organizations to help consumers, small businesses, and their employees navigate the process of finding and obtaining health coverage options through the Marketplace. This training enables Navigators to assist with completing eligibility and enrollment forms, thereby facilitating access to affordable and comprehensive health coverage for uninsured consumers.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the role of biometric systems in access control, requiring similar depth and breadth of explanation.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What's the role of oversight and feedback in managing GAI risks and communicating their societal effects?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI model explanation', 'GAI risks', 'Privacy risk assessment', 'Data provenance', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the dangers arising from the intersection of pervasive surveillance practices and the unregulated collection of personal data on the American populace. It is specific in its focus on the American context and the combination of surveillance and data collection. The intent is clear, seeking an analysis of the multifaceted dangers involved. The question is self-contained and does not rely on external references or prior knowledge beyond general understanding of surveillance and data collection practices.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of data provenance in the context of AI model documentation and governance?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of content provenance in mitigating unique risks associated with GAI, as highlighted by stakeholder consultations. It is clear in specifying the topic of interest (content provenance, GAI risks) and the context (stakeholder consultations). However, it assumes familiarity with the specific stakeholder consultations and the unique risks they highlighted, which are not provided within the question. To improve clarity and answerability, the question could include a brief description of the unique risks identified by stakeholders or specify the context of these consultations.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What role does content provenance play in mitigating unique risks associated with GAI, as highlighted by stakeholder consultations?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the precautions or steps needed to ensure the safe use of derived data in automated systems, with a focus on avoiding bias. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated traffic control systems', 'Smart city technologies', 'Fraud detection algorithms', 'Biometric systems', 'Access control algorithms']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What risks come from widespread surveillance and unregulated data collection on Americans?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'AI Bill of Rights', 'Civil rights and liberties', 'Equal opportunities', 'Access to critical resources']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of having a human fallback system in automated processes. It is clear in its intent, seeking an explanation of the importance or benefits of such a system. The question is independent and does not rely on external references or additional context to be understood. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the significance of having a human fallback system in automated processes?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI incidents', 'AI Actors', 'Incident reporting', 'Documentation practices', 'AI risk management']
[ragas.testset.evolutions.INFO] seed question generated: "What are the responsible uses of synthetic data in GAI development?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Notice and explanation', 'Impact on lives', 'Opaque decision-making', 'Algorithmic risk assessment']
[ragas.testset.evolutions.INFO] seed question generated: "What role do fraud detection algorithms play in the adjudication of benefits and penalties?"
[ragas.testset.evolutions.INFO] seed question generated: "What types of systems are considered under the category of equal opportunities in the context of the AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does incident reporting play in improving GAI risk management across the AI ecosystem?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of information sharing and feedback mechanisms specifically in relation to GAI systems, while the second question addresses the broader role of oversight and feedback in managing GAI risks and communicating their societal effects. The scope and depth of the inquiries differ.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about issues arising from system flaws in benefit allocation. It is clear in its intent, seeking information on the problems caused by flaws in the system used for allocating benefits. The question is independent and does not rely on external references or additional context to be understood. It is specific enough to be answerable by someone with knowledge in the domain of benefit allocation systems.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What issues arise from system flaws in benefit allocation?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key aspects of designing explainable AI as discussed in a specific panel on Artificial Intelligence and Democratic Values. While it specifies the topic (explainable AI) and the context (a panel discussion), it assumes access to the content of the panel discussion without providing any details or summary of what was discussed. This makes the question unclear for those who did not attend the panel or do not have access to its proceedings. To improve clarity and answerability, the question could include a brief summary of the key points or themes discussed in the panel, or alternatively, frame the question in a way that does not rely on specific, unpublished discussions.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are the key aspects of designing explainable AI as discussed in the panel on Artificial Intelligence and Democratic Values?"
[ragas.testset.evolutions.INFO] seed question generated: "What challenges do algorithmic risk assessments pose for individuals in understanding and contesting decisions that affect their lives?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Information integrity', 'Human-AI configuration', 'Digital content transparency', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Discrimination in mortgage lending', 'Redlining initiative', 'Algorithmic decision-making', 'Healthcare access disparities', 'Bias in artificial intelligence']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the risks associated with surveillance and data collection on the American public, requiring similar depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the purpose of verifying the effectiveness of carbon capture programs in relation to GAI (presumably General Artificial Intelligence) training and applications. While it specifies the topic of interest (carbon capture programs and GAI), it is somewhat ambiguous due to the lack of clarity on how these two areas are related. The term 'GAI' is not commonly used and could be confusing without further context. To improve clarity and answerability, the question could benefit from specifying what 'GAI' stands for and explaining the connection between carbon capture programs and AI training or applications. For example, it could ask how carbon capture programs impact the environmental footprint of AI training or how they are integrated into AI applications.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are the concerns associated with harmful bias and homogenization in the context of GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What initiatives is the federal government implementing to combat discrimination in mortgage lending?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of data provenance in the context of AI model documentation and governance. It is clear in specifying the topic of interest (data provenance) and the context (AI model documentation and governance). The intent is to understand the importance or role of data provenance within this specific context. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the significance of data provenance in the context of AI model documentation and governance?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of content provenance in mitigating unique risks associated with GAI, as highlighted by stakeholder consultations. It is clear in specifying the topic of interest (content provenance, GAI risks) and the context (stakeholder consultations). However, it assumes familiarity with the specific stakeholder consultations and the unique risks they highlighted, which are not provided within the question. To improve clarity and answerability, the question could include a brief description of the unique risks identified by stakeholders or specify the context of these consultations.', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What complications arise in benefit distribution when automated systems operate without transparency and clear explanations?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the responsible uses of synthetic data in the development of General Artificial Intelligence (GAI). It is clear in specifying the topic of interest (responsible uses of synthetic data) and the context (GAI development). The intent is straightforward, seeking information on ethical or appropriate applications of synthetic data within this specific domain. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the responsible uses of synthetic data in GAI development?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Environmental impacts of GAI', 'Harmful bias in AI systems', 'Generative AI energy consumption', 'Disparities in model performance', 'Trustworthy AI characteristics']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the challenges posed by algorithmic risk assessments for individuals in terms of understanding and contesting decisions that impact their lives. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on the difficulties individuals face with these assessments.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What challenges do algorithmic risk assessments pose for individuals in understanding and contesting decisions that affect their lives?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of incident reporting in improving GAI (General Artificial Intelligence) risk management across the AI ecosystem. It is clear in specifying the topic of interest (incident reporting) and the context (GAI risk management within the AI ecosystem). The intent is to understand the impact or contribution of incident reporting to risk management, which is straightforward and does not rely on external references or unspecified contexts. Therefore, the question is specific, independent, and has a clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does incident reporting play in improving GAI risk management across the AI ecosystem?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of fraud detection algorithms in the adjudication of benefits and penalties. It is clear in its intent, seeking to understand the impact or function of these algorithms within a specific context (adjudication of benefits and penalties). The question is self-contained and does not rely on external references or prior knowledge not provided within the question itself. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the roles of human fallback in automated systems and its impact on public access. It is clear in specifying the topic of interest (human fallback in automated systems) and seeks information on both the roles and the impact on public access. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. However, it could be improved by specifying what is meant by 'public access' (e.g., access to services, information, or technology) to ensure a more precise and relevant response.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Oversight and feedback play a crucial role in managing GAI risks by ensuring that organizational policies and practices are in place to collect, consider, prioritize, and integrate feedback from external sources regarding the potential individual and societal impacts related to AI risks. This includes establishing oversight functions across the GAI lifecycle and documenting the risks and potential impacts of the AI technology, which facilitates broader communication about these impacts.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the types of systems considered under the category of equal opportunities in the context of the AI Bill of Rights. It is clear in specifying the topic of interest (equal opportunities systems) and the context (AI Bill of Rights). The intent is clear, seeking information on the classification of systems within a specific framework. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What types of systems are considered under the category of equal opportunities in the context of the AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the environmental impacts associated with the energy consumption of generative AI systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does data provenance play in ensuring the ethical governance and documentation of AI models, particularly in relation to human subject protection and bias management?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI system context', 'Harmful bias and homogenization', 'Interdisciplinary AI actors', 'Risk measurement plans', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the initiatives the federal government is implementing to combat discrimination in mortgage lending. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge and clearly seeks information on government actions against discrimination in mortgage lending.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What initiatives is the federal government implementing to combat discrimination in mortgage lending?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What are the roles of human fallback in automated systems and its impact on public access?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of using structured feedback mechanisms in relation to AI-generated content. It is clear in specifying the topic of interest (structured feedback mechanisms) and the context (AI-generated content). The intent is straightforward, seeking an explanation of the purpose or benefits of these mechanisms. The question is self-contained and does not rely on external references or prior knowledge beyond what is provided in the question itself.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the key aspects of designing explainable AI as discussed in a specific panel on Artificial Intelligence and Democratic Values. While it specifies the topic (explainable AI) and the context (a panel discussion), it assumes the reader has access to or knowledge of the content of this specific panel discussion. This reliance on external references makes the question unclear for those who did not attend or have access to the panel's details. To improve clarity and answerability, the question could either provide a brief summary of the panel's main points or reframe to ask about general key aspects of designing explainable AI without relying on the specific panel discussion.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI incidents', 'AI Actors', 'Incident reporting', 'Documentation practices', 'AI risk management']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about concerns related to harmful bias and homogenization within the context of GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (harmful bias and homogenization) and the context (GAI systems), making the intent clear and understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it independent and answerable based on domain knowledge.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the concerns associated with harmful bias and homogenization in the context of GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What factors should be assessed to determine the expected and acceptable GAI system context of use?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Fraud detection algorithms assist in the adjudication of benefits and penalties by analyzing information and matching records to support decision-makers.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does documentation play in improving AI risk management across the AI ecosystem?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What impact does the systematic documentation and reporting of GAI incidents have on the evaluation and enhancement of risk management practices among AI Actors within the ecosystem?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about practices that ensure synthetic data aligns with real-world statistics while protecting privacy. It is clear in its intent, seeking specific practices or methods. The question is self-contained and does not rely on external references or unspecified contexts. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Ongoing monitoring', 'Clear organizational oversight', 'High-quality data', 'Governance procedures']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What federal initiatives are being undertaken to address algorithmic biases in mortgage lending practices, particularly concerning communities of color?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What practices ensure synthetic data aligns with real-world stats while protecting privacy?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the importance and role of human fallback systems in automated processes, sharing the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the complications in benefit distribution when automated systems lack transparency and clear explanations. It is specific in its focus on 'benefit distribution' and the conditions of 'automated systems' operating without 'transparency and clear explanations'. The intent is clear, seeking information on the potential issues or challenges that arise under these conditions. The question is self-contained and does not rely on external references or prior knowledge beyond general understanding of automated systems and transparency. Therefore, it meets the criteria for clarity and answerability.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of data provenance in the ethical governance and documentation of AI models, with a focus on human subject protection and bias management. It is specific and clear in its intent, seeking information on how data provenance contributes to these particular aspects of AI ethics. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the factors that contribute to individuals' difficulties in contesting algorithmic decisions affecting their lives. It is clear in its intent, seeking specific information about the challenges faced by individuals in this context. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Equitable design', 'Automated systems', 'Legal protections', 'Proactive equity assessments']
[ragas.testset.evolutions.INFO] seed question generated: "What should be included in the governance procedures for the development and use of automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What implications arise from the interplay of harmful bias and content uniformity in GAI systems, particularly regarding data accuracy and user feedback mechanisms?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the environmental impacts associated with the energy consumption of generative AI systems. It is specific in its focus on environmental impacts and energy consumption, and it is clear in its intent to understand the relationship between these two factors. The question is self-contained and does not rely on external references or additional context to be understood or answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the environmental impacts associated with the energy consumption of generative AI systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of using structured feedback mechanisms in relation to AI-generated content is to solicit and capture user input about the content to detect subtle shifts in quality or alignment with community and societal values.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What issues come up in benefit distribution with opaque automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does data provenance support ethical AI governance, especially for human protection and bias?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks which automated systems impact equal opportunities in education, housing, and employment. It is clear in its intent, seeking information on specific types of automated systems and their effects on equal opportunities in these areas. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What factors contribute to individuals' difficulties in contesting algorithmic decisions affecting their lives?"
[ragas.testset.evolutions.INFO] seed question generated: "What actions are suggested to address risks associated with intellectual property infringement in organizational GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do legal protections play in addressing algorithmic discrimination?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "Which automated systems impact equal opportunities in education, housing, and employment?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Cyberattacks', 'Intellectual Property', 'Obscene and abusive content', 'CBRN weapons', 'Chemical and biological design tools']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the responsible uses of synthetic data in GAI development, while the second question is concerned with aligning synthetic data with real statistics and ensuring privacy. These questions have different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of documentation in improving AI risk management across the AI ecosystem. It is clear in specifying the topic of interest (documentation) and the context (AI risk management within the AI ecosystem). The intent is straightforward, seeking an explanation of how documentation contributes to risk management. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role does documentation play in improving AI risk management across the AI ecosystem?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for factors to be assessed in determining the expected and acceptable GAI (General Artificial Intelligence) system context of use. It is clear in its intent, seeking specific factors for assessment. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What factors should be assessed to determine the expected and acceptable GAI system context of use?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the impact of systematic documentation and reporting of GAI (General AI Incidents) on the evaluation and enhancement of risk management practices among AI Actors within the ecosystem. It is clear in specifying the topic of interest (systematic documentation and reporting of GAI incidents) and the context (evaluation and enhancement of risk management practices among AI Actors). The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'White House Office of Science and Technology Policy', 'Automated systems', 'Civil rights and democratic values', 'National security and defense activities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about federal initiatives aimed at addressing algorithmic biases in mortgage lending practices, with a particular focus on communities of color. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on government actions in a specific area of concern.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address issues arising from flaws in benefit allocation systems, with a focus on automated systems. They share the same depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What role do chemical and biological design tools play in augmenting design capabilities in chemistry and biology?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What are the energy-related environmental consequences of generative AI systems, particularly in relation to their potential to perpetuate harmful biases and produce undesirable content?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does documenting GAI incidents affect AI risk management?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the significance of data provenance in AI model documentation and governance, while the second question emphasizes how data provenance supports ethical AI governance, particularly in terms of human protection and bias. These questions have different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically addresses the challenges posed by algorithmic risk assessments in understanding and contesting decisions, while the second question is broader and focuses on the general difficulty of challenging algorithmic decisions. The depth and breadth of the inquiries differ.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What role do civil rights and democratic values play in the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the elements that should be included in governance procedures for the development and use of automated systems. It is clear in its intent, seeking specific information about governance procedures, and does not rely on external references or unspecified contexts. The question is self-contained and understandable, making it answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What should be included in the governance procedures for the development and use of automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What federal steps are being taken to tackle algorithmic bias in mortgage lending for communities of color?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of harmful bias and content uniformity in GAI (General Artificial Intelligence) systems, specifically focusing on data accuracy and user feedback mechanisms. It is clear in specifying the aspects of interest (harmful bias, content uniformity, data accuracy, user feedback mechanisms) and seeks detailed information on their interplay. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of legal protections in addressing algorithmic discrimination. It is clear in its intent, seeking information on how legal frameworks can mitigate or address issues related to algorithmic bias. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role do legal protections play in addressing algorithmic discrimination?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to address risks associated with intellectual property infringement in organizational GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (intellectual property infringement risks) and the context (organizational GAI systems). The intent is to seek actionable suggestions, making it specific and understandable without requiring additional context or external references.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the types of systems considered under the category of equal opportunities in the context of the AI Bill of Rights, which is a broader inquiry. The second question specifically asks about automated systems affecting equal opportunities in education, housing, and jobs, which is more specific and narrower in scope.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What interdisciplinary factors and ongoing evaluations should be considered to assess the anticipated and acceptable context of use for GAI systems, particularly in relation to socio-cultural impacts and data integrity?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What are the effects of bias and uniformity in GAI on data accuracy and user feedback?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 3, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Incident Database', 'Generative AI security flaws', 'Large Language Models', 'Ethical Tensions in Human-AI Companionship', 'Disinformation Business of Chinese Influence Operations']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Consider opportunities to responsibly use synthetic data and other privacy enhancing techniques in GAI development, where appropriate and applicable, to match the statistical properties of real-world data without disclosing personally identifiable information or contributing to homogenization.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the impact of incident reporting or documenting GAI incidents on AI risk management, sharing the same depth, breadth, and requirements for the answer.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not explicitly mention how data provenance supports ethical AI governance, particularly regarding human protection and bias.', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Incident Database?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address discrimination in mortgage lending, the first question is broader, encompassing all forms of discrimination, whereas the second question specifically focuses on algorithmic bias affecting communities of color. This leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What governance elements should be integrated to ensure ongoing public safety and effective oversight in the development and deployment of automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What measures are essential to ensure that automated systems are designed to prevent algorithmic discrimination while also safeguarding community safety and effectiveness?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of chemical and biological design tools in augmenting design capabilities in chemistry and biology. It is clear in specifying the topic of interest (chemical and biological design tools) and seeks information on their impact on design capabilities within these fields. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role do chemical and biological design tools play in augmenting design capabilities in chemistry and biology?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the energy-related environmental consequences of generative AI systems, with a particular focus on their potential to perpetuate harmful biases and produce undesirable content. While the question is clear in its intent to explore the environmental impact and ethical concerns of generative AI, it conflates two distinct issues: environmental consequences and ethical implications. To improve clarity and answerability, the question could be split into two separate queries: one focusing on the environmental impact of generative AI systems and another on their potential to perpetuate biases and produce undesirable content.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are the energy-related environmental consequences of generative AI systems, particularly in relation to their potential to perpetuate harmful biases and produce undesirable content?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'People find it hard to challenge algorithmic decisions because they are often denied the knowledge needed to address the impact of automated systems on their lives. The decision-making processes of these systems tend to be opaque and complex, making it difficult for individuals to ascertain how or why a decision was made. Additionally, the lack of clear and timely explanations can hinder their ability to contest decisions effectively.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connections between GAI incident documentation and AI risk management effectiveness. While it specifies the two areas of interest (GAI incident documentation and AI risk management effectiveness), it lacks clarity on what 'GAI' stands for, which could be ambiguous for those not familiar with the term. Additionally, the question does not specify what kind of connections it is interested in (e.g., statistical correlations, procedural impacts, case studies). To improve clarity and answerability, the question could define 'GAI' and specify the type of connections it seeks to explore.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What connections exist between GAI incident documentation and AI risk management effectiveness?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The suggested action to address risks associated with intellectual property infringement in organizational GAI systems is to compile statistics on actual policy violations, take-down requests, and intellectual property infringement, and analyze transparency reports across demographic and language groups.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of civil rights and democratic values in the Blueprint for an AI Bill of Rights. It is clear in specifying the topic of interest (civil rights and democratic values) and the document in question (Blueprint for an AI Bill of Rights). The intent is clear, seeking an explanation of the relationship or influence of these values within the specified document. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role do civil rights and democratic values play in the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['CBRN Information', 'Confabulation', 'Dangerous content', 'Data Privacy', 'Harmful Bias']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.evolutions.INFO] seed question generated: "What is confabulation and how does it mislead users?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question seeks to identify interdisciplinary factors and ongoing evaluations necessary to assess the anticipated and acceptable context of use for GAI (General Artificial Intelligence) systems, with a focus on socio-cultural impacts and data integrity. It is clear in its intent, specifying the areas of interest (socio-cultural impacts and data integrity) and the type of information sought (interdisciplinary factors and evaluations). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Incident Database. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific database.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the AI Incident Database?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Safety Institute established by NIST?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Automated systems', 'Bias testing', 'Equitable design', 'Systemic biases']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems that affect equal opportunities in education include algorithms that detect student cheating or plagiarism, admissions algorithms, online or virtual reality student monitoring systems, projections of student progress or outcomes, algorithms that determine access to resources or programs, and surveillance of classes. In housing, tenant screening algorithms, automated valuation systems for mortgage underwriting or home insurance, and automated valuations from online aggregator websites are relevant. For employment, workplace algorithms that inform terms and conditions of employment, hiring or termination algorithms, virtual or augmented reality workplace training programs, and electronic workplace surveillance and management systems are included.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What factors should be considered for assessing GAI systems' socio-cultural impacts and data integrity?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for governance elements that should be integrated to ensure public safety and effective oversight in the development and deployment of automated systems. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on governance elements related to public safety and oversight in the context of automated systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on concerns related to harmful bias and homogenization in GAI systems, while the second question specifically addresses the effects of bias and uniformity on data accuracy and user feedback. These questions have different scopes and depths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for essential measures to ensure that automated systems are designed to prevent algorithmic discrimination while also safeguarding community safety and effectiveness. It is clear in its intent, specifying the dual goals of preventing discrimination and ensuring safety and effectiveness. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What significance do civil rights and democratic principles hold in the framework designed to guide the ethical deployment of automated systems as outlined in the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The federal government is working to combat discrimination in mortgage lending through initiatives such as the Department of Justice's nationwide initiative to combat redlining. This includes reviewing how lenders may be avoiding serving communities of color and conducting targeted marketing and advertising. Additionally, the Action Plan to Advance Property Appraisal and Valuation Equity includes a commitment from agencies overseeing mortgage lending to include a nondiscrimination standard in proposed rules for Automated Valuation Models.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures are being taken to ensure equitable design in automated systems to protect against algorithmic discrimination?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What governance aspects are key for public safety in automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps ensure automated systems avoid bias and maintain safety?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated sentiment analyzer', 'Bias against Jews and gay people', 'Search engine results for minority groups', 'Advertisement delivery systems and stereotypes', 'Algorithmic discrimination in healthcare']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about specialized AI systems that enhance design in chemistry and biology beyond traditional methods. It is clear in specifying the domain (chemistry and biology) and the context (enhancing design beyond traditional methods). The intent is to identify specific AI systems and their contributions, making it understandable and answerable without needing additional context or external references.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI trustworthy characteristics', 'Human-AI Configuration', 'Information Integrity', 'Data Privacy', 'Confabulation']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Risk Management Framework', 'Generative AI', 'Cross-sectoral profile', 'Risk management priorities', 'Large language models']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the energy-related environmental consequences of generative AI systems, with a particular focus on their potential to perpetuate harmful biases and produce undesirable content. While the question is clear in its intent to explore the environmental impact and ethical concerns of generative AI, it conflates two distinct issues: environmental consequences and ethical implications. To improve clarity and answerability, the question could be split into two separate queries: one focusing on the energy-related environmental consequences and another on the ethical concerns related to biases and undesirable content. This would make each question more specific and easier to address independently.', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does the AI Incident Database play in addressing the challenges posed by AI in cybersecurity and mental health?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connections between GAI incident documentation and AI risk management effectiveness. While it specifies the two areas of interest (GAI incident documentation and AI risk management effectiveness), it lacks clarity on what 'GAI' stands for, which could be ambiguous for those not familiar with the term. Additionally, it does not specify the type of connections or the context in which these connections should be evaluated. To improve clarity and answerability, the question could define 'GAI' and specify the type of connections (e.g., causal, correlational) and the context (e.g., within a specific industry or study).", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on determining the context of use for GAI systems, while the second question is concerned with assessing socio-cultural impacts and data integrity. These are different areas of inquiry with distinct requirements and depths.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for an explanation of 'confabulation' and how it misleads users. It is clear in specifying the term of interest (confabulation) and seeks detailed information on both the definition and the impact on users. The question is self-contained and does not rely on external references or prior knowledge beyond understanding the term 'confabulation'. Therefore, it meets the criteria of independence and clear intent.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is confabulation and how does it mislead users?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What specialized AI systems enhance design in chemistry and biology beyond traditional methods?"
[ragas.testset.evolutions.INFO] seed question generated: "What issues does the automated sentiment analyzer address regarding bias in online statements?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Safety Institute established by NIST. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific institute.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the AI Safety Institute established by NIST?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Equal Opportunities and Civil Justice', 'Impact of technology on equity', 'AI systems and access limitations', 'Surveillance concerns', 'Community input in technology design']
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to ensure information integrity in the deployment of GAI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the comprehensive governance procedures for the development and use of automated systems, while the second question specifically targets governance aspects related to public safety. This difference in focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the cross-sectoral profile in the context of the AI Risk Management Framework for Generative AI?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the ways in which AI systems are being used to limit access to equal opportunities in education, housing, and employment?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Continuous monitoring of GAI system impacts', 'Harmful bias and homogenization', 'Structured human feedback exercises', 'GAI red-teaming', 'Information integrity']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the measures being taken to ensure equitable design in automated systems to protect against algorithmic discrimination. It is clear in its intent, specifying the focus on 'equitable design' and 'algorithmic discrimination'. The question is independent and does not rely on external references or prior knowledge not included within the question itself. Therefore, it is understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What measures are being taken to ensure equitable design in automated systems to protect against algorithmic discrimination?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the role of legal protections in addressing algorithmic discrimination, while the second question is broader, asking about steps to avoid bias and maintain safety in automated systems. They differ in both depth and breadth of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of civil rights and democratic principles within the framework for the ethical deployment of automated systems as outlined in the AI Bill of Rights. It is clear in specifying the topic of interest (civil rights and democratic principles) and the context (AI Bill of Rights). The intent is also clear, seeking an explanation of the importance of these principles within the specified framework. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The effects of bias and uniformity in GAI on data accuracy and user feedback are related to harmful bias and homogenization, which can compromise the representativeness and relevance of data used in AI systems. This can lead to inaccuracies in the information generated and may affect the quality of user feedback, as it may not accurately reflect diverse perspectives or experiences.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the role of chemical and biological design tools in augmenting design capabilities, while the second question specifically asks about AI systems that improve design in chemistry and biology. The scope and depth of the inquiries differ.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of the AI Incident Database in addressing challenges related to AI in cybersecurity and mental health. It is clear in specifying the database and the two areas of interest (cybersecurity and mental health), making the intent understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it independent and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What erroneous content generation, often termed confabulation, can lead to user deception, particularly in the context of accessing sensitive information or capabilities related to CBRN weapons?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do civil rights and democracy fit into the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What objectives does the U.S. AI Safety Institute aim to achieve in relation to the standards and frameworks for managing AI risks as outlined by NIST?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of structured human feedback exercises in the context of GAI risk measurement and management?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issues that an automated sentiment analyzer addresses concerning bias in online statements. It is clear in specifying the topic of interest (automated sentiment analyzer) and the specific aspect (bias in online statements). The intent is clear, and the question is self-contained, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure information integrity in the deployment of GAI (General Artificial Intelligence) systems. It is clear in its intent, seeking specific measures or strategies related to information integrity. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Factors to consider for assessing GAI systems' socio-cultural impacts include assumptions and limitations, direct value to the organization, intended operational environment, observed usage patterns, potential positive and negative impacts to individuals and communities, and social norms and expectations. For data integrity, factors include evaluating the quality and integrity of data used in training, the provenance of AI-generated content, and ensuring that data or benchmarks used in risk measurement are representative of diverse in-context user populations.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does the AI Incident Database help with AI challenges in cybersecurity and mental health?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Key governance aspects for public safety in automated systems include laying out clear governance structures and procedures, establishing responsibility for oversight, involving organizational stakeholders in governance procedures, and ensuring that those in charge are aware of potential impacts on people's rights and opportunities. Additionally, it may be appropriate to conduct an independent ethics review before deployment.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Executive Order 13960', 'Trustworthy Artificial Intelligence', 'AI Bill of Rights', 'NIST AI Risk Management Framework', 'Stakeholder engagement']
[ragas.testset.evolutions.INFO] seed question generated: "What actions are suggested to address risks associated with intellectual property infringement in organizational GAI systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What proactive strategies are being implemented to ensure that automated systems are designed and utilized in a manner that prevents unjust treatment based on protected characteristics?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for ways in which AI systems are being used to limit access to equal opportunities in education, housing, and employment. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on the negative impacts of AI systems in these specific areas.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the ways in which AI systems are being used to limit access to equal opportunities in education, housing, and employment?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the cross-sectoral profile within the context of the AI Risk Management Framework for Generative AI. It is clear in specifying the topic of interest (cross-sectoral profile) and the context (AI Risk Management Framework for Generative AI), making the intent clear and understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it specific and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the role of civil rights and democratic values in the context of the AI Bill of Rights, requiring similar depth and breadth of explanation.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What principles are required for the design and use of trustworthy artificial intelligence in the federal government?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human subject protection', 'Content provenance', 'Data privacy', 'AI system performance', 'Anonymization techniques']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Chemical and biological design tools (BDTs) are highly specialized AI systems trained on scientific data that aid in chemical and biological design, potentially improving design capabilities beyond what text-based LLMs can provide.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To ensure automated systems avoid bias and maintain safety, designers, developers, and deployers should take proactive and continuous measures, including conducting proactive equity assessments as part of system design, using representative data, ensuring accessibility for people with disabilities, performing pre-deployment and ongoing disparity testing and mitigation, and maintaining clear organizational oversight. Additionally, independent evaluation and reporting should confirm that the system is safe and effective, including steps taken to mitigate potential harms.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for the general purpose of the AI Incident Database, while the second question specifically inquires about its role in addressing AI challenges in cybersecurity and mental health, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of structured human feedback exercises specifically in the context of GAI (General Artificial Intelligence) risk measurement and management. It is clear in specifying the topic of interest (structured human feedback exercises) and the context (GAI risk measurement and management), making the intent clear and the question self-contained. No additional context or external references are needed to understand or answer the question.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of structured human feedback exercises in the context of GAI risk measurement and management?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to protect data privacy in evaluations involving human subjects?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about erroneous content generation, specifically confabulation, and its potential to deceive users in the context of accessing sensitive information or capabilities related to CBRN (Chemical, Biological, Radiological, and Nuclear) weapons. The intent is clear, seeking to understand the risks and implications of confabulation in this specific context. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the objectives of the U.S. AI Safety Institute in relation to the standards and frameworks for managing AI risks as outlined by NIST. It is specific in mentioning the U.S. AI Safety Institute and NIST, and it clearly seeks information about the objectives related to AI risk management standards and frameworks. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Monitoring system capabilities', 'GAI content interaction', 'Content provenance', 'User feedback integration', 'AI incident tracking']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "In what ways do AI technologies contribute to the reinforcement of inequities in access to education, housing, and employment, while also potentially exacerbating burdens on individuals interacting with social welfare systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Suggested measures to ensure information integrity in the deployment of GAI systems include verifying GAI system training data and TEVV data provenance, and ensuring that fine-tuning or retrieval-augmented generation data is grounded. Additionally, it is recommended to review and verify sources and citations in GAI system outputs during pre-deployment risk measurement and ongoing monitoring activities.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The automated sentiment analyzer addresses bias in online statements by identifying that it was found to be biased against Jews and gay people. For instance, it marked the statement 'I’m a Jew' as negative while identifying 'I’m a Christian' as positive. This bias could lead to the preemptive blocking of social media comments such as 'I’m gay.'", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Synthetic training data', 'Model collapse', 'Environmental impact', 'GAI systems', 'Carbon capture programs']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to address risks associated with intellectual property infringement in organizational GAI (General Artificial Intelligence) systems. It is specific in its focus on intellectual property infringement and organizational GAI systems, and it clearly seeks actionable recommendations. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What actions are suggested to address risks associated with intellectual property infringement in organizational GAI systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What confabulation might mislead users about CBRN info or capabilities?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the cross-sectoral profile in the context of the AI Risk Management Framework for Generative AI is to assist organizations in deciding how to best manage AI risks in a manner that aligns with their goals, considers legal/regulatory requirements and best practices, and reflects risk management priorities. It offers insights into how risk can be managed across various stages of the AI lifecycle and for Generative AI as a technology.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does user feedback integration play in enhancing the monitoring process for GAI models?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about proactive strategies to ensure automated systems are designed and used to prevent unjust treatment based on protected characteristics. It does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. The intent is clear, seeking information on strategies and measures in place for ethical design and use of automated systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Preventable harms', 'Ethics review', 'Sepsis prediction model', 'Algorithmic bias']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What are the U.S. AI Safety Institute's goals for NIST's AI risk standards?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of ballot curing laws in the voting process?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of assessing the proportion of synthetic to non-synthetic training data in AI model development?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the principles required for the design and use of trustworthy artificial intelligence in the federal government. It is clear in specifying the topic of interest (trustworthy artificial intelligence) and the context (federal government). The intent is straightforward, seeking information on the principles that should be followed. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps are taken to ensure fair use of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI models', 'Synthetic NCII and CSAM', 'Trustworthy AI Characteristics', 'Value Chain and Component Integration', 'GAI risks management']
[ragas.testset.evolutions.INFO] seed question generated: "What are some of the potential harms associated with automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to protect data privacy in evaluations involving human subjects. It is clear in its intent, specifying the context (evaluations involving human subjects) and the type of information sought (measures to protect data privacy). The question is independent and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What combined strategies are recommended for mitigating intellectual property risks in GAI systems while ensuring transparency and accountability in their deployment?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about confabulation in general and its misleading effects, while the second question specifically focuses on how confabulation might mislead users about CBRN (Chemical, Biological, Radiological, and Nuclear) information or capabilities. The scope and context differ significantly.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What role do civil rights play in the context of automated systems and technology according to the foreword?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the challenges associated with value chain and component integration in GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Data privacy', 'Privacy by design', 'Data collection limitations', 'Risk identification and mitigation']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear in its intent, asking for specific ways AI technologies contribute to reinforcing inequities in access to education, housing, and employment, and how they might exacerbate burdens on individuals interacting with social welfare systems. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The question is specific and seeks detailed information on the negative impacts of AI technologies in these areas.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question asks about the purpose of the AI Safety Institute established by NIST, while the second question inquires about the goals of the U.S. AI Safety Institute specifically related to NIST's AI risk standards. Although related, the questions have different focuses and requirements.", 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically addresses equitable design and protection against algorithmic discrimination, while the second question broadly addresses fair use without specifying discrimination. This leads to different depths and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the roles of structured feedback and red-teaming in General Artificial Intelligence (GAI) risk management. It is clear in specifying the two elements of interest (structured feedback and red-teaming) and the context (GAI risk management). The intent is to understand the contributions of these elements to managing risks associated with GAI. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of ballot curing laws in the voting process. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks an explanation of the purpose of these laws.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How might AI tech reinforce inequities in education, housing, and jobs, and add burdens on those using social welfare?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of user feedback integration in enhancing the monitoring process for GAI (General Artificial Intelligence) models. It is clear in specifying the topic of interest (user feedback integration) and the context (monitoring process for GAI models). The intent is to understand the impact or contribution of user feedback on the monitoring process. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does user feedback integration play in enhancing the monitoring process for GAI models?"
[ragas.testset.evolutions.INFO] seed question generated: "What should entities do to proactively identify and manage risks associated with collecting, using, sharing, or storing sensitive data?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology in social welfare', 'Fraud detection', 'Digital ID systems', 'Healthcare access and delivery', 'Health disparities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of assessing the proportion of synthetic to non-synthetic training data in AI model development. It is clear in specifying the topic of interest (proportion of synthetic to non-synthetic training data) and seeks information on its importance in the context of AI model development. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What roles do structured feedback and red-teaming play in GAI risk mgmt?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The principles required for the design and use of trustworthy artificial intelligence in the federal government include: (a) lawful and respectful of our Nation’s values; (b) purposeful and performance-driven; (c) accurate, reliable, and effective; (d) safe, secure, and resilient; (e) understandable; (f) responsible and traceable; (g) regularly monitored; (h) transparent; and (i) accountable.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about potential harms associated with automated systems. It is clear in its intent, seeking information on the negative impacts or risks of automated systems. The question is independent and does not rely on external references or specific prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What concerns were raised by panelists regarding healthcare access and delivery in relation to new technologies?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Suggested measures to protect data privacy in evaluations involving human subjects include: anonymizing data to protect the privacy of human subjects, leveraging privacy output filters, removing any personally identifiable information (PII) to prevent potential harm or misuse, and providing human subjects with options to withdraw participation or revoke their consent for present or future use of their data in GAI applications.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Confabulation in the context of CBRN information or capabilities refers to the production of confidently stated but erroneous or false content that may mislead or deceive users regarding the access to or synthesis of nefarious information or design capabilities related to CBRN weapons or other dangerous materials.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for combined strategies to mitigate intellectual property risks in GAI (General Artificial Intelligence) systems while ensuring transparency and accountability in their deployment. It is clear in specifying the topic of interest (intellectual property risks, GAI systems) and the desired outcome (strategies for mitigation, transparency, and accountability). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the impact of AI on equity in education, housing, and employment, but the second question also includes social welfare, adding a broader scope.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the challenges associated with value chain and component integration in GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (value chain and component integration) and the context (GAI systems). The intent is to understand the difficulties or obstacles in these areas. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "How does the integration of user feedback into GAI monitoring enhance the effectiveness of provenance tracking and risk management strategies?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'User consent', 'Automated systems', 'Surveillance technologies', 'Sensitive domains']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of civil rights in the context of automated systems and technology, specifically according to 'the foreword'. While it is clear in specifying the topic of interest (civil rights, automated systems, technology) and the source of information (the foreword), it assumes access to and understanding of 'the foreword' without providing its content or context. This makes the question unclear for those without direct access to the foreword. To improve clarity and answerability, the question could include a brief description or key points from the foreword, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What role do civil rights play in the context of automated systems and technology according to the foreword?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the role of structured feedback in the context of GAI risk management, though the second question also includes red-teaming. However, the depth and breadth of the inquiry are similar as both focus on risk management strategies.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies help manage IP risks in GAI while ensuring transparency?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['TEVV metrics', 'Measurement error models', 'GAI system risks', 'Feedback processes', 'Harmful bias and homogenization']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The importance of assessing the proportion of synthetic to non-synthetic training data in AI model development is to verify that the training data is not overly homogenous or generated by Generative AI (GAI), which helps mitigate concerns of model collapse.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does user consent play in the collection and use of personal data according to data privacy guidelines?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data protection', 'Third-party considerations', 'Risk management', 'Pre-deployment testing', 'GAI systems']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Ballot curing laws are designed to allow voters to correct their ballot and have it counted in cases where a voter signature matching algorithm incorrectly flags their ballot as invalid or when there are other issues with their ballot. These laws ensure that voters have a fallback system to verify the validity of their ballot, which may include direct contact from election officials.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Identity theft', 'Facial recognition system', 'Surveillance software', 'Employee discussions about union activity']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of establishing feedback processes for end users and impacted communities in AI system evaluation metrics?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about concerns raised by panelists regarding healthcare access and delivery in relation to new technologies. It is clear in specifying the topic of interest (concerns, healthcare access and delivery, new technologies) and seeks detailed information on the concerns raised. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What concerns were raised by panelists regarding healthcare access and delivery in relation to new technologies?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Many companies, non-profits, and federal government agencies are taking steps to ensure the public is protected from algorithmic discrimination. Some companies have instituted bias testing as part of their product quality assessment and launch procedures, which has led to changes or prevented harmful product launches. Federal agencies are developing standards and guidance for the use of automated systems to help prevent bias. Non-profits and companies have developed best practices for audits and impact assessments to identify potential algorithmic discrimination and provide transparency in mitigating such biases.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of pre-deployment testing in the AI lifecycle?"
[ragas.testset.evolutions.INFO] seed question generated: "What concerns arise from companies using surveillance software to track employee discussions about union activity?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Automated systems', 'Human fallback', 'Critical protections', 'Voting process']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Some potential harms associated with automated systems include: reliance on unproven technologies that may not work as intended, causing substantial and unjustified harm; the use of historical data that can lead to irrelevant information affecting decision-making; technologies designed to violate safety, such as those facilitating stalking; unintended harms from intended or unintended uses; and issues like alert fatigue from false alerts, as seen in a sepsis prediction model. Additionally, automated moderation systems may fail to distinguish between counter-speech and hateful messages, silencing critics.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address intellectual property risks in GAI systems, the first question focuses on suggested actions, whereas the second question emphasizes strategies that also ensure transparency. This difference in focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking what actions entities should take to proactively identify and manage risks related to the collection, use, sharing, or storage of sensitive data. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking practical steps or strategies for risk management in the context of sensitive data handling.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the impact of integrating user feedback into GAI (General Artificial Intelligence) monitoring on the effectiveness of provenance tracking and risk management strategies. It is clear in specifying the elements of interest (user feedback, GAI monitoring, provenance tracking, risk management) and seeks to understand the enhancement in effectiveness. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does human oversight play in the voting process, particularly regarding automated signature matching systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What efforts is NIST making to ensure the development of safe and trustworthy AI?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'AI technology can reinforce inequities in education, housing, and jobs by being used to limit access to equal opportunities, such as through automated tenant background screening, discrimination in automated hiring screening, and remote proctoring systems. Additionally, these technologies can shift the burden of oversight from employers to workers, schools to students, and landlords to tenants, which diminishes equality of opportunity. In the context of social welfare, AI systems may reduce the burden for government agencies but increase the burden on individuals interacting with these technologies, potentially creating feedback loops that reinforce inequality.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Ethical use of AI systems', 'Department of Energy AI Advancement Council', 'Artificial Intelligence Ethical Principles', 'National Science Foundation research', 'Pretrial risk assessments transparency']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Challenges associated with value chain and component integration in GAI systems include the improper acquisition or vetting of third-party components such as datasets, pre-trained models, and software libraries, which can lead to diminished transparency and accountability. The scale of training data may be too large for humans to vet, and the difficulty of training foundation models can result in extensive reuse of a limited number of models. Additionally, it may be difficult to attribute issues in a system's behavior to any one of these sources, and errors in third-party GAI components can have downstream impacts on accuracy and robustness.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does user feedback improve GAI monitoring for tracking and risk management?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of user consent in the collection and use of personal data according to data privacy guidelines. It is clear in specifying the topic of interest (user consent, personal data, data privacy guidelines) and seeks detailed information on the role of user consent. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does user consent play in the collection and use of personal data according to data privacy guidelines?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What issues did panelists identify regarding the intersection of new technologies and healthcare access, particularly in terms of equity and community involvement?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of civil rights in the context of automated systems and technology, specifically according to 'the foreword'. While it is clear in specifying the topic of interest (civil rights, automated systems, technology) and the source of information (the foreword), it assumes access to and understanding of 'the foreword' without providing its content or context. This makes the question unclear for those without direct access to the foreword. To improve clarity and answerability, the question could include a brief description or key points from the foreword, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Civil rights and liberties', 'Equal opportunities', 'Access to critical resources']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Sensitive data', 'Ethical review', 'Data quality', 'Access limitations']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of establishing feedback processes for end users and impacted communities in the context of AI system evaluation metrics. It is clear in specifying the topic of interest (feedback processes, end users, impacted communities, AI system evaluation metrics) and seeks an explanation of the rationale behind these processes. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of establishing feedback processes for end users and impacted communities in AI system evaluation metrics?"
[ragas.testset.evolutions.INFO] seed question generated: "What types of research does the National Science Foundation support to ensure the safety and effectiveness of automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not provide specific strategies for managing IP risks in GAI while ensuring transparency.', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the concerns related to companies using surveillance software to monitor employee discussions about union activity. It does not rely on external references or prior knowledge and has a clear intent, seeking information on the potential issues or problems that may arise from such practices.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of pre-deployment testing in the AI lifecycle. It is specific and clear in its intent, seeking information on the role and significance of pre-deployment testing within the broader context of AI development and deployment. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What criteria does the framework use to determine which automated systems are in scope for the AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for handling sensitive data in automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Entities that collect, use, share, or store sensitive data should attempt to proactively identify harms and seek to manage them to avoid, mitigate, and respond appropriately to identified risks. Appropriate responses include determining not to process data when the privacy risks outweigh the benefits or implementing measures to mitigate acceptable risks.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI trustworthy characteristics', 'Human-AI Configuration', 'Information Integrity', 'Data Privacy', 'Confabulation', 'GAI systems', 'Digital content transparency', 'Harmful bias', 'Content provenance', 'AI system trustworthiness']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the role of user feedback in improving the monitoring process for GAI models, specifically in the context of tracking and risk management. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Biometric Information Privacy Act', 'Transparency for machine learning systems', 'Adverse action notices', 'Explainable AI systems', 'California warehouse employee quotas']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of human oversight in the voting process, specifically in relation to automated signature matching systems. It is clear in its intent, seeking information on the interaction between human oversight and automated systems within a specific context (voting process). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role does human oversight play in the voting process, particularly regarding automated signature matching systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What significance does user consent hold in the context of safeguarding personal data against abusive practices and ensuring ethical use in sensitive domains?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology in social welfare', 'Fraud detection', 'Digital ID systems', 'Healthcare access and delivery', 'Health disparities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the efforts NIST (National Institute of Standards and Technology) is making to ensure the development of safe and trustworthy AI. It is specific, independent, and has a clear intent, seeking information on NIST's initiatives or actions in this area. The question does not rely on external references or prior knowledge beyond the general understanding of NIST and AI, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the suggested actions to improve Human-AI configuration in GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are adverse action notices and what requirements do lenders have regarding them?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Executive Order 13960', 'Trustworthy Artificial Intelligence', 'AI Bill of Rights', 'NIST AI Risk Management Framework', 'Stakeholder engagement']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.evolutions.INFO] seed question generated: "What role do digital ID systems play in improving efficiency and reducing costs in social welfare?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issues identified by panelists concerning the intersection of new technologies and healthcare access, with a focus on equity and community involvement. It is specific in its scope (new technologies and healthcare access) and clearly states the aspects of interest (equity and community involvement). The intent is clear, seeking information on the issues identified by panelists. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Concerns arise from companies using surveillance software to track employee discussions about union activity, as it leads to the surveillance of individual employees and allows companies to surreptitiously intervene in discussions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question is clear and specific, asking about the types of research supported by the National Science Foundation (NSF) to ensure the safety and effectiveness of automated systems. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on NSF-supported research areas related to automated systems' safety and effectiveness.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What types of research does the National Science Foundation support to ensure the safety and effectiveness of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI incidents', 'AI Actors', 'Incident reporting', 'Documentation practices', 'AI risk management']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the NIST AI Risk Management Framework?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for handling sensitive data in automated systems. It is clear in its intent, seeking information on best practices or standards for managing sensitive data within such systems. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the expectations for handling sensitive data in automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What challenges did panelists see at the tech-health equity intersection?"
[ragas.testset.evolutions.INFO] seed question generated: "What efforts is NIST making to ensure the safe and trustworthy development of artificial intelligence?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the criteria used by a framework to determine which automated systems fall under the scope of the AI Bill of Rights. It is clear in its intent, seeking specific information about the criteria used for inclusion. The question is independent and does not rely on external references or prior knowledge beyond what is stated. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The importance of pre-deployment testing in the AI lifecycle lies in its ability to complicate risk mapping and pre-deployment measurement efforts due to the diverse ways and contexts in which GAI systems may be developed, used, and repurposed. Robust test, evaluation, validation, and verification (TEVV) processes can be iteratively applied and documented in the early stages of the AI lifecycle, ensuring that the systems are properly assessed before deployment.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of documenting and reporting GAI incidents for AI Actors?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the roles of user feedback and community input in assessing AI system risks. It is clear in specifying the elements of interest (user feedback, community input) and the context (assessing AI system risks). The intent is straightforward, seeking an explanation of the contributions of these factors to risk assessment in AI systems. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What roles do user feedback and community input play in assessing AI system risks?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of user consent in safeguarding personal data against abusive practices and ensuring ethical use in sensitive domains. It is clear in specifying the topic of interest (user consent) and the context (safeguarding personal data, ethical use in sensitive domains). The intent is to understand the role and importance of user consent in these areas. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What NSF-funded research initiatives align with federal principles for ensuring the ethical deployment and effectiveness of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for an explanation of adverse action notices and the requirements lenders have regarding them. It is clear in specifying the topic of interest (adverse action notices) and seeks detailed information on both the definition and the regulatory requirements for lenders. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are adverse action notices and what requirements do lenders have regarding them?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'NIST is making efforts to ensure the development of safe and trustworthy AI by developing measurements, technology, tools, and standards that advance reliable, safe, transparent, explainable, privacy-enhanced, and fair artificial intelligence. They have established the U.S. AI Safety Institute and the AI Safety Institute Consortium to build the necessary science for safe, secure, and trustworthy development and use of AI, in alignment with the 2023 Executive Order on Safe, Secure, and Trustworthy AI.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to improve Human-AI configuration in GAI (General Artificial Intelligence) systems. It is clear in its intent, seeking specific actions or recommendations for improvement. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. However, it could benefit from specifying the aspects of Human-AI configuration it is interested in (e.g., collaboration, decision-making, user interface) to narrow down the scope and provide more targeted answers.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the suggested actions to improve Human-AI configuration in GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['CBRN Information', 'Confabulation', 'Dangerous content', 'Data Privacy', 'Harmful Bias']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on concerns related to healthcare access and delivery with new technologies, while the second question is broader, addressing challenges at the intersection of technology and health equity. These questions differ in both depth and breadth.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the safeguards that ensure human oversight in the process of automated signature matching during voting. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information about the mechanisms or procedures in place to maintain human oversight in this automated process.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of digital ID systems in improving efficiency and reducing costs in social welfare. It is clear in specifying the topic of interest (digital ID systems) and the context (social welfare), and it seeks specific information on their impact on efficiency and cost reduction. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role do digital ID systems play in improving efficiency and reducing costs in social welfare?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "Why is user consent important for protecting personal data?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the NIST AI Risk Management Framework. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific framework.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What safeguards ensure human oversight in automated signature matching during voting?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the impacts of data privacy related to the unauthorized use or disclosure of sensitive information?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the efforts made by NIST to ensure the safe and trustworthy development of artificial intelligence. It is specific in identifying the organization (NIST) and the topic of interest (safe and trustworthy development of AI). The intent is clear, seeking information on the actions or initiatives undertaken by NIST in this area. The question is independent and does not rely on external references or prior knowledge beyond what is provided in the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What efforts is NIST making to ensure the safe and trustworthy development of artificial intelligence?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The framework uses a two-part test to determine which automated systems are in scope for the AI Bill of Rights: (1) automated systems that (2) have the potential to meaningfully impact the American public’s rights, opportunities, or access to critical resources or services.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI considerations', 'Governance principles', 'Generative AI risks', 'Organizational governance', 'AI value chain']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of establishing feedback processes, while the second question is about how feedback and input assess AI risks. They have different focuses and depths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what automated systems must ensure regarding consent and ethical review for sensitive data. It is clear in its intent, seeking specific information about the requirements or standards for automated systems in the context of handling sensitive data. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does the 2023 Executive Order on Safe AI play in NIST's efforts to develop trustworthy artificial intelligence?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of documenting and reporting GAI (General Artificial Intelligence) incidents for AI Actors. It is clear in specifying the topic of interest (GAI incidents) and the target audience (AI Actors). The intent is to understand the significance of these actions, which is straightforward and unambiguous. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the importance of documenting and reporting GAI incidents for AI Actors?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What legal obligations do lenders have to inform consumers about adverse actions taken based on automated decision-making systems, and how does this relate to the broader need for transparency in algorithmic processes affecting individual rights?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the specific role of user consent in the context of data privacy guidelines, while the second question asks about the importance of user consent in protecting personal data. The depth and breadth of the inquiries differ.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What considerations are important for governing across the AI value chain in the context of generative AI?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What must automated systems ensure regarding consent and ethical review for sensitive data?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for NSF-funded research initiatives that align with federal principles for ensuring the ethical deployment and effectiveness of automated systems. It is clear in specifying the type of research (NSF-funded) and the criteria for alignment (federal principles for ethical deployment and effectiveness of automated systems). The intent is clear, and the question is specific and independent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about the role of human oversight in the context of automated signature matching systems, while the second question broadly asks about ensuring human oversight in automated voting signatures without specifying the context. This leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Regulatory safety requirements', 'Civil rights and civil liberties', 'Technical standards and practices', 'Fair Information Practice Principles']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "Which NSF projects align with federal ethics for automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What initiatives is NIST undertaking to promote the secure and reliable advancement of AI, particularly in relation to the frameworks and standards outlined in their recent publications?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Panelists discussed several challenges at the tech-health equity intersection, including access to and expense of broadband service, privacy concerns associated with telehealth systems, and the expense associated with health monitoring devices, which can exacerbate equity issues. They also highlighted the need for accountability in the technologies used in medical care, particularly regarding racial biases and the use of race in medicine, which perpetuate harms and embed prior discrimination.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the NIST AI Risk Management Framework is to help manage risks posed to individuals, organizations, and society by AI. It aims to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does the documentation and reporting of GAI incidents play in enhancing the evaluation and management of AI system performance by AI Actors?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the regulatory safety requirements for medical devices in relation to the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the impacts of data privacy related to the unauthorized use or disclosure of sensitive information. It is clear in specifying the topic of interest (data privacy impacts) and the context (unauthorized use or disclosure of sensitive information). The intent is straightforward, seeking information on the consequences or effects of such privacy breaches. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the impacts of data privacy related to the unauthorized use or disclosure of sensitive information?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the connections between digital ID systems, efficiency in welfare, and potential community burdens. It is clear in specifying the three elements of interest (digital ID systems, welfare efficiency, and community burdens) and seeks to understand their interrelationships. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the expectations for handling sensitive data, while the second question emphasizes considerations for consent and ethics. These are related but distinct aspects, leading to different depths and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about actions that can enhance GAI (General Artificial Intelligence) systems' Human-AI configuration while ensuring information integrity and security. The intent is clear in seeking specific actions or strategies. However, the term 'Human-AI config' is somewhat ambiguous and could benefit from clarification. Additionally, the question could be more specific about what aspects of information integrity and security are of interest (e.g., data privacy, system robustness). To improve clarity, the question could specify what is meant by 'Human-AI config' and detail the particular concerns regarding information integrity and security.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What actions enhance GAI systems' Human-AI config while ensuring info integrity and security?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'User feedback and community input assess AI risks through established feedback processes that allow end users and impacted communities to report problems and appeal system outcomes. These processes are integrated into AI system evaluation metrics, which include conducting impact assessments on how AI-generated content might affect different social, economic, and cultural groups, as well as understanding user perceptions and interactions with GAI content.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of the 2023 Executive Order on Safe AI in NIST's efforts to develop trustworthy artificial intelligence. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information on the impact of a specific executive order on NIST's AI development efforts.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between digital ID systems, efficiency in welfare, and potential community burdens?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Sensitive domains', 'Predictive analytics', 'Student data collection', 'Employee data transfer']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the safety and effectiveness of automated systems, while the second question is concerned with the alignment of NSF projects with federal ethics. These are different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the legal obligations of lenders to inform consumers about adverse actions taken based on automated decision-making systems and how this relates to the broader need for transparency in algorithmic processes affecting individual rights. It is clear in specifying the topic of interest (legal obligations, automated decision-making systems, transparency) and seeks detailed information on both the legal requirements and the broader implications for transparency. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'User consent is important for protecting personal data because it ensures that data collection is justified only in cases where consent can be appropriately and meaningfully given. Consent requests should be brief, understandable in plain language, and provide individuals with agency over their data collection and its specific context of use. This approach helps to prevent abusive data practices and ensures that individuals have control over how their data is used.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about important considerations for governing across the AI value chain specifically in the context of generative AI. It is clear in specifying the topic of interest (governance, AI value chain, generative AI) and seeks detailed information on governance considerations. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'A human curing process helps voters confirm their signatures and correct other voting mistakes, ensuring that all votes are counted. This process is already standard practice in much of the country, allowing both an election official and the voter to review and correct any issues.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Artificial Intelligence and Democratic Values', 'Non-discriminatory technology', 'Explainable AI', 'Community participation', 'Social welfare systems']
[ragas.testset.evolutions.INFO] seed question generated: "What are the concerns associated with student data collection in educational settings?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What must lenders disclose to consumers about adverse actions from automated decisions, and how does this tie into the need for transparency in algorithms affecting rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does community participation play in the design of technology for democratic values?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the initiatives NIST is undertaking to promote secure and reliable AI advancement, specifically in relation to frameworks and standards mentioned in their recent publications. It is clear in specifying the organization (NIST) and the focus (secure and reliable AI), and it seeks information on initiatives and related frameworks and standards. However, the question assumes familiarity with 'recent publications' without specifying which publications or providing context about them. To improve clarity and answerability, the question could specify the titles or key points of the recent publications or provide a brief description of the relevant frameworks and standards.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What initiatives is NIST undertaking to promote the secure and reliable advancement of AI, particularly in relation to the frameworks and standards outlined in their recent publications?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Safety Institute established by NIST?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses specifically on the role of digital ID systems in improving efficiency and reducing costs in social welfare, while the second question is broader, asking about the links between digital IDs, welfare efficiency, and community impacts. This difference in scope and focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of documentation and reporting of GAI (General AI) incidents in enhancing the evaluation and management of AI system performance by AI Actors. It is clear in specifying the topic of interest (documentation and reporting of GAI incidents) and the context (evaluation and management of AI system performance by AI Actors). The intent is clear, seeking to understand the impact of these practices on AI system performance management. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the relationship between specific sensitive data leaks and their corresponding privacy impacts. While the intent is clear in seeking information on the consequences of data leaks, the question is somewhat broad and could benefit from more specificity. For example, it could specify types of sensitive data (e.g., financial, medical) or particular privacy impacts (e.g., identity theft, financial loss). This would make the question more focused and easier to answer comprehensively.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The 2023 Executive Order on Safe, Secure, and Trustworthy AI plays a significant role in NIST's efforts by guiding the establishment of the U.S. AI Safety Institute and the AI Safety Institute Consortium, which are aimed at building the necessary science for the safe, secure, and trustworthy development and use of AI.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are some real-life examples of how human alternatives can be implemented in various sectors?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the regulatory safety requirements for medical devices in relation to the AI Bill of Rights. It is clear in specifying the topic of interest (regulatory safety requirements for medical devices) and the context (AI Bill of Rights). However, the AI Bill of Rights is a broad and potentially ambiguous term that could refer to different documents or initiatives depending on the jurisdiction or context. To improve clarity and answerability, the question could specify which AI Bill of Rights it refers to (e.g., a specific country's legislation or a particular organization's guidelines).", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are the regulatory safety requirements for medical devices in relation to the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about actions that can enhance GAI (General Artificial Intelligence) systems' Human-AI configuration while ensuring information integrity and security. The intent is clear in seeking specific actions or strategies. However, the term 'Human-AI config' is somewhat ambiguous and could benefit from clarification. Additionally, the question could be more specific about what aspects of information integrity and security are of interest (e.g., data privacy, system robustness). To improve clarity, the question could specify what is meant by 'Human-AI config' and detail the particular concerns regarding information integrity and security.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems should consider that consent for sensitive data may need to be acquired from a guardian and/or child, and that consent for non-necessary functions should be optional. Additionally, any use of sensitive data or decision processes based on sensitive data that might limit rights, opportunities, or access should undergo a thorough ethical review and monitoring. This includes ensuring that data quality is maintained to avoid adverse consequences from flawed data, limiting access to sensitive data based on necessity, and providing regular public reports on data security lapses and ethical pre-reviews.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on adverse action notices and the requirements for lenders, while the second question delves into disclosures about adverse actions from automated decisions and the broader context of algorithmic transparency. The second question has a broader scope and depth.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The National Science Foundation (NSF) funds extensive research to help foster the development of automated systems that adhere to and advance their safety, security, and effectiveness. Multiple NSF programs support research that directly addresses many of these principles, including the National AI Research Institutes, the Cyber Physical Systems program, the Secure and Trustworthy Cyberspace program, the Formal Methods in the Field program, and the Designing Accountable Software Systems program.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What sensitive data leaks lead to which specific privacy impacts?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do GAI incident docs help AI Actors assess and manage system performance?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the concerns associated with student data collection in educational settings. It is clear in its intent, seeking information on potential issues or risks related to this practice. The question is specific and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the concerns associated with student data collection in educational settings?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of community participation in the design of technology for democratic values. It is clear in specifying the topic of interest (community participation, technology design, democratic values) and seeks information on the relationship between these elements. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The important considerations for governing across the AI value chain in the context of generative AI include organizational governance, oversight levels, human-AI configurations, human review, tracking and documentation, and management oversight. Additionally, governance tools and protocols that apply to other types of AI systems can also be applied to generative AI systems, including accessibility, AI actor credentials, alignment to organizational values, auditing, change-management controls, commercial use, and data provenance.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Prompt injection', 'Indirect prompt injection attacks', 'Data poisoning', 'Intellectual property risks', 'Obscene and degrading content']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Safety Institute established by NIST. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific institute.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Incident response plans', 'Third-party GAI technologies', 'Data privacy', 'Continuous monitoring', 'Vendor contracts']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the importance of documenting and reporting GAI incidents, while the second question is about how these documents help in assessing and managing system performance. They have different focuses and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the impacts of data privacy related to unauthorized use or disclosure of sensitive information, which is broader and more detailed. The second question specifically asks about data leaks causing privacy issues, which is narrower in scope.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the initiatives NIST is undertaking to promote secure and reliable AI advancement, specifically in relation to frameworks and standards mentioned in their recent publications. It is clear in specifying the organization (NIST) and the focus (secure and reliable AI advancement), and it seeks information on initiatives, frameworks, and standards. However, it assumes familiarity with NIST's recent publications without providing specific details or context about these documents. To improve clarity and answerability, the question could benefit from mentioning specific frameworks or standards or providing a brief description of the recent publications in question.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are indirect prompt injection attacks and how do they exploit vulnerabilities in GAI-integrated applications?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data Privacy', 'Privacy Act of 1974', 'NIST Privacy Framework', 'Biometric identifying technology', 'Workplace surveillance']
[ragas.testset.evolutions.INFO] seed question generated: "What are the key considerations for establishing incident response plans for third-party GAI technologies?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how human alternatives can be implemented in various sectors. It is clear in its intent, seeking specific examples across different sectors. However, the term 'human alternatives' is somewhat vague and could benefit from clarification. Does it refer to automation, AI, robotics, or other forms of technology replacing human roles? To improve clarity and answerability, the question could specify what is meant by 'human alternatives' and possibly provide examples of sectors of interest.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are some real-life examples of how human alternatives can be implemented in various sectors?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the regulatory safety requirements for medical devices in relation to the AI Bill of Rights. It is clear in specifying the topic of interest (regulatory safety requirements for medical devices) and the context (AI Bill of Rights). However, the AI Bill of Rights is a broad and potentially ambiguous term that could refer to different documents or initiatives depending on the jurisdiction or context. To improve clarity and answerability, the question could specify which AI Bill of Rights it refers to (e.g., a specific country's legislation or a particular organization's guidelines).", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Panelists described the increasing scope of technology use in providing for social welfare, including digital ID systems, which are focused on improving efficiency and reducing cost. However, they cautioned that these systems may reduce the burden for government agencies by increasing the burden and agency of people using and interacting with these technologies. Additionally, these systems can produce feedback loops and compounded harm, collecting data from communities and using it to reinforce inequality. To mitigate these harms, it was suggested that community input should be ensured at the beginning of the design process, and there should be ways to opt out of these systems and use associated human-driven mechanisms instead.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the requirements for employers regarding workplace surveillance during a labor dispute?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Preventable harms', 'Ethics review', 'Sepsis prediction model', 'Algorithmic bias']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Lenders are required by federal law to notify consumers about certain decisions made about them, specifically through "adverse action" notices. This includes providing notice of the reasons a creditor took adverse action on a credit application or existing credit account. This requirement ties into the need for transparency in algorithms affecting rights, as it ensures that consumers are informed about the automated decisions impacting their credit, allowing them to understand and contest those decisions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Prompt injection', 'Indirect prompt injection attacks', 'Data poisoning', 'Intellectual property risks', 'Obscene and degrading content']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Community participation plays a crucial role in the design of technology for democratic values by emphasizing human-computer interaction that involves the community, ensuring that the technology is non-discriminatory, explainable, and privacy-aware. Engaging with impacted communities helps to understand the potential harms of technologies and build protection by design into future systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-enabled nudification technology', 'Image-based abuse', 'Non-consensual intimate images', 'AI-powered cameras', 'Road safety habits']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the AI Safety Institute established by NIST is to continue efforts to build the science necessary for safe, secure, and trustworthy development and use of artificial intelligence (AI), in alignment with the 2023 Executive Order on Safe, Secure, and Trustworthy AI.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issues that arise from collecting sensitive student data and its potential misuse. It is clear in its intent, seeking information on the problems associated with data collection and misuse. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['CBRN Information', 'Confabulation', 'Dangerous content', 'Data Privacy', 'Harmful Bias']
[ragas.testset.evolutions.INFO] seed question generated: "What role does an ethics review play in the development of automated systems to prevent harm?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential consequences of prompt injection attacks on GAI systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions impacts due to leakage and unauthorized use, disclosure, or de-anonymization of biometric, health, location, or other personally identifiable information or sensitive data as causes of privacy issues.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What problems does AI-enabled nudification technology seek to address and protect against?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What issues arise from collecting sensitive student data and its potential misuse?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Policies and procedures for human-AI configurations', 'Oversight of GAI systems', 'Risk measurement processes', 'Human-AI configuration', 'Threat modeling for GAI systems']
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of eased access to dangerous content?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Automated systems', 'Bias testing', 'Equitable design', 'Systemic biases', 'Algorithmic discrimination', 'Equitable design', 'Automated systems', 'Legal protections', 'Proactive equity assessments']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about indirect prompt injection attacks and how they exploit vulnerabilities in GAI-integrated applications. It is specific and clear in its intent, seeking an explanation of a particular type of attack and its impact on a defined context (GAI-integrated applications). The question is self-contained and does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are indirect prompt injection attacks and how do they exploit vulnerabilities in GAI-integrated applications?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['OSTP', 'Artificial intelligence', 'Biometric technologies', 'Request For Information (RFI)', 'Public comments']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Automated systems', 'Timely human consideration', 'Fallback and escalation process', 'Sensitive domains']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'GAI incident documentation helps AI Actors assess and manage system performance by facilitating smoother sharing of information regarding incidents, which includes logging, recording, and analyzing GAI incidents. This documentation allows AI Actors to trace impacts to their source, understand previous incidents, and implement measures to prevent similar occurrences in the future. Additionally, regular information sharing and maintaining change management records empower AI Actors in responding to and managing AI incidents effectively.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key considerations in establishing incident response plans for third-party GAI (General Artificial Intelligence) technologies. It is specific in its focus on incident response plans and third-party GAI technologies, and it clearly seeks information on the considerations involved in this process. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the key considerations for establishing incident response plans for third-party GAI technologies?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how human alternatives can be implemented in various sectors. It is clear in its intent, seeking specific examples across different sectors. However, the term 'human alternatives' is somewhat vague and could benefit from clarification. Does it refer to automation, AI, or other technological replacements for human roles? Specifying this would improve the clarity and answerability of the question.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are the policies and procedures related to human-AI configuration in the oversight of AI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.INFO] seed question generated: "What role do legal protections play in addressing algorithmic discrimination?"
[ragas.testset.evolutions.INFO] seed question generated: "What was the purpose of the Request For Information (RFI) issued by OSTP regarding biometric technologies?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the requirements for employers regarding workplace surveillance during a labor dispute. It is specific and clear in its intent, seeking information on legal or regulatory requirements. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of labor disputes and workplace surveillance. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "While both questions address the collection of student data, 'concerns associated with student data collection in educational settings' is broader and includes a wider range of issues than 'risks of collecting sensitive student data', leading to different depths of inquiry.", 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Technical protections', 'Rights of the American public', 'Implementation of principles']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential consequences of prompt injection attacks on GAI (Generative AI) systems. It is clear in specifying the type of attack (prompt injection) and the target (GAI systems), and it seeks information on the consequences of such attacks. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the potential consequences of prompt injection attacks on GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Reporting expectations', 'Transparency', 'Artificial Intelligence ethics', 'Traffic calming measures', 'AI Risk Management Framework']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the problems that AI-enabled nudification technology aims to address and protect against. It is clear in its intent, seeking specific information about the objectives and protective measures of this technology. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What problems does AI-enabled nudification technology seek to address and protect against?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What mechanisms underlie indirect prompt injection attacks in GAI systems, and how do these mechanisms facilitate the exploitation of vulnerabilities in applications that integrate large language models?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of an ethics review in the development of automated systems to prevent harm. It is clear in specifying the topic of interest (ethics review) and the context (development of automated systems to prevent harm). The intent is straightforward, seeking an explanation of the role and importance of ethics reviews in this specific context. The question is self-contained and does not rely on external references or prior knowledge beyond general understanding of ethics reviews and automated systems.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role does an ethics review play in the development of automated systems to prevent harm?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Stakeholder meetings', 'Private sector and civil society', 'Positive use cases', 'Potential harms and oversight']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Clear and accessible notice', 'Explanations for decisions', 'Algorithmic impact assessments', 'User experience research']
[ragas.testset.evolutions.INFO] seed question generated: "What role do technical protections play in the implementation of the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question 'What are the implications of eased access to dangerous content?' is too vague and broad. It does not specify what type of dangerous content is being referred to (e.g., violent media, harmful substances, misinformation), nor does it provide a context for 'eased access' (e.g., through the internet, social media, physical availability). To improve clarity and answerability, the question could specify the type of dangerous content and the context in which access is being eased. For example, 'What are the implications of eased access to violent media content through social media platforms?'", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are the implications of eased access to dangerous content?"
[ragas.testset.evolutions.INFO] seed question generated: "What were some of the discussions related to positive use cases during the meetings conducted by OSTP?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do algorithmic impact assessments play in the expectations for automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of legal protections in addressing algorithmic discrimination. It is clear in specifying the topic of interest (legal protections) and the issue it addresses (algorithmic discrimination). The intent is straightforward, seeking an explanation of how legal measures can mitigate or address biases in algorithms. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of legal protections and algorithmic discrimination.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for using automated systems in sensitive domains. It is clear in its intent, seeking information on factors to consider, and does not rely on external references or unspecified contexts. The question is specific and independent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the policies and procedures related to human-AI configuration in the oversight of AI systems. It is clear in specifying the topic of interest (policies and procedures, human-AI configuration, oversight of AI systems) and seeks detailed information on these aspects. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the Request For Information (RFI) issued by the Office of Science and Technology Policy (OSTP) regarding biometric technologies. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of the RFI.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What issues does AI-driven nudification tech aim to mitigate, and how do these relate to broader concerns about automated systems causing unintended harm?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Federal law requires employers, and any consultants they may retain, to report the costs of surveilling employees in the context of a labor dispute. Employers engaging in workplace surveillance aimed at obtaining information concerning the activities of employees or a labor organization in connection with a labor dispute must report expenditures relating to this surveillance to the Department of Labor Office of Labor-Management Standards, and consultants who employers retain for these purposes must also file reports regarding their activities.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The risks of collecting sensitive student data include concerns about the lack of express parental consent, the lack of transparency in how the data is being used, and the potential for resulting discriminatory impacts. Additionally, the data collected can include sensitive information such as demographic details, drug use, and interest in LGBTQI+ groups, which may lead to inappropriate forecasting of student success and flagging of students with disabilities as cheating.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the mechanisms underlying indirect prompt injection attacks in GAI (Generative AI) systems and how these mechanisms facilitate the exploitation of vulnerabilities in applications that integrate large language models. It is specific in its focus on indirect prompt injection attacks and the exploitation of vulnerabilities, making the intent clear. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the steps to ensure effective incident response for third-party GAI (General Artificial Intelligence), specifically focusing on linking ownership and legal alignment. The intent is clear as it seeks specific steps or measures. However, the term 'GAI' might be ambiguous without further context, and the question could benefit from a brief explanation of what is meant by 'third-party GAI'. Additionally, the phrase 'linking ownership and legal alignment' could be clarified to specify what aspects of ownership and legal alignment are of interest (e.g., data ownership, liability, compliance).", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What multifaceted risks arise from prompt injection attacks on GAI systems, particularly concerning misinformation dissemination and the potential for data poisoning?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What factors must be evaluated to ensure effective human oversight and alternatives when deploying automated systems in high-stakes areas like criminal justice and healthcare?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of technical protections in the implementation of the Blueprint for an AI Bill of Rights. It is clear in specifying the topic of interest (technical protections) and the context (Blueprint for an AI Bill of Rights). The intent is to understand the specific contributions or functions of technical protections within this framework. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role do technical protections play in the implementation of the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the safeguards ensured by ethics reviews to prevent harm in automated systems. It is clear in specifying the topic of interest (safeguards, ethics reviews, harm prevention, automated systems) and seeks detailed information on the measures taken. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology (NIST). It does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. The intent is clear, seeking information about the purpose of a specific framework from a specific organization.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What drives indirect prompt injection in GAI systems and how do they exploit app vulnerabilities?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What steps ensure effective incident response for third-party GAI, linking ownership and legal alignment?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination', 'AI Bill of Rights', 'Automated systems', 'American people', 'October 2022']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of algorithmic impact assessments in the expectations for automated systems. It is clear in specifying the topic of interest (algorithmic impact assessments) and the context (expectations for automated systems). The intent is to understand the significance or influence of these assessments on automated systems. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role do algorithmic impact assessments play in the expectations for automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight of AI systems. This includes establishing acceptable use policies for GAI interfaces, modalities, and human-AI configurations, as well as defining criteria for the kinds of queries GAI applications should refuse to respond to.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions that algorithmic discrimination may violate legal protections, indicating that legal protections play a role in addressing algorithmic discrimination by providing a framework that designers, developers, and deployers of automated systems must adhere to in order to protect individuals and communities from unjustified different treatment based on various classifications.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about discussions related to positive use cases during meetings conducted by OSTP. It is clear in specifying the topic of interest (positive use cases) and the context (meetings conducted by OSTP). However, it assumes familiarity with the specific meetings and their content without providing additional context or details about which meetings or time frame are being referred to. To improve clarity and answerability, the question could specify the particular meetings or time period of interest, or provide more context about the discussions being referred to.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What were some of the discussions related to positive use cases during the meetings conducted by OSTP?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What safeguards are ensured by ethics reviews to prevent harm in automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question 'What are the implications of eased access to dangerous content?' is too vague and broad. It does not specify the type of dangerous content (e.g., violent, illegal, harmful misinformation) or the context in which access is eased (e.g., online platforms, physical media). Additionally, 'implications' could refer to a wide range of potential effects (e.g., societal, psychological, legal). To improve clarity and answerability, the question could specify the type of dangerous content and the context of access, as well as the specific implications of interest.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the Request For Information (RFI) issued by OSTP regarding biometric technologies was to understand the extent and variety of biometric technologies in past, current, or planned use; the domains in which these technologies are being used; the entities making use of them; current principles, practices, or policies governing their use; and the stakeholders that are, or may be, impacted by their use or regulation.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What issues related to bias and discrimination are associated with the use of automated systems in decision-making?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about indirect prompt injection attacks in GAI systems and how they exploit vulnerabilities in applications, sharing the same depth, breadth, and requirements for the answer.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for automated systems regarding safety and effectiveness?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the issues that AI-driven nudification technology aims to mitigate and how these issues relate to broader concerns about automated systems causing unintended harm. It is clear in specifying the technology of interest (AI-driven nudification tech) and seeks information on both the specific issues it addresses and the broader implications. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. However, the term 'nudification' might be unfamiliar to some readers, so a brief definition or context could enhance clarity further.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Human alternatives', 'Opt-out mechanism', 'Timely human consideration', 'Fallback and escalation system']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks for key considerations in establishing incident response plans, implying a need for detailed steps or factors. The second question is broader, asking what ensures effective incident response, which could include considerations but also other elements like tools, training, and policies. Thus, they differ in depth and breadth.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What significance do algorithmic impact assessments hold in shaping the clarity and accountability expectations for automated systems across varying risk levels?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for the factors that need to be evaluated to ensure effective human oversight and alternatives when deploying automated systems in high-stakes areas like criminal justice and healthcare. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking a list or discussion of relevant factors for effective oversight and alternatives in these specific domains.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Confabulation', 'Generative AI systems', 'False content', 'Statistical prediction', 'Risks of confabulated content']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions pertain to ethics reviews in the context of automated systems, the first question focuses on the role of ethics reviews in preventing harm, whereas the second question is more general, asking about the safeguards provided by ethics reviews. This difference in focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What problems does AI nudification tech address, and how do they connect to wider concerns about automated harm?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of timely human consideration in the context of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the connections between technical protections and the rights outlined in the AI Bill of Rights. It is clear in its intent, seeking to understand the relationship between two specific concepts: technical protections and the AI Bill of Rights. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. However, for improved clarity, it could specify which AI Bill of Rights it refers to, as there might be different versions or interpretations.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Actors', 'GAI system performance', 'Content provenance data tracking', 'Incident response plans', 'Human-AI Configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key aspects that ensure transparency in AI systems according to the NIST framework. It is specific in its focus on transparency and the NIST framework, making the intent clear. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What factors ensure effective oversight in automated systems for critical fields like justice and healthcare?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with confabulated content in generative AI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated system', 'Plain language documentation', 'System functioning', 'Outcome explanations', 'User notification']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between technical protections and the rights outlined in the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about issues related to bias and discrimination associated with the use of automated systems in decision-making. It is clear in its intent, seeking information on specific problems (bias and discrimination) within a defined context (automated systems in decision-making). The question is independent and does not rely on external references or additional context to be understood. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of incident response plans in managing GAI system incidents?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key aspects ensure transparency in AI systems as per the NIST framework?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Technical protections', 'Rights of the American public', 'Implementation of principles']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for automated systems in terms of safety and effectiveness. It is clear in its intent, seeking information on the criteria or standards for these systems. The question is independent and does not rely on external references or unspecified contexts. Therefore, it is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the expectations for automated systems regarding safety and effectiveness?"
[ragas.testset.evolutions.INFO] seed question generated: "What should users be notified about regarding automated systems that impact them?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the problems AI-enabled nudification technology seeks to address and protect against, while the second question also includes a broader inquiry into how these problems connect to wider concerns about automated harm. This difference in scope and depth makes the questions not equal.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What are the main principles outlined in the AI Bill of Rights and how do they aim to protect the rights of the American public?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Ethics reviews provide safeguards for automated systems by vetting key development decisions to prevent harm from occurring. They help identify and mitigate potential harms through pre-deployment testing and ongoing monitoring processes.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of algorithmic impact assessments in shaping clarity and accountability expectations for automated systems across varying risk levels. It is specific in its focus on algorithmic impact assessments and their role in clarity and accountability. The intent is clear, seeking an explanation of the importance and influence of these assessments. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address the use of automated systems in sensitive or critical domains, the first question is broader, asking for general considerations, whereas the second question specifically focuses on factors ensuring effective oversight. This difference in focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Effective incident response for third-party GAI is ensured by establishing incident response plans that align with impacts, communicating these plans to relevant AI actors, defining ownership of incident response functions, rehearsing the plans regularly, improving them based on retrospective learning, and reviewing for alignment with relevant laws.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of timely human consideration in the context of automated systems. It is clear in specifying the topic of interest (timely human consideration) and the context (automated systems). The intent is to understand the significance of human intervention or oversight in automated processes. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the importance of timely human consideration in the context of automated systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about the role of technical protections in the implementation of the Blueprint for an AI Bill of Rights, while the second question is broader, asking about the links between tech protections and the AI Bill of Rights. The depth and breadth of the inquiries differ.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question asks for the purpose of the AI Risk Management Framework by NIST, which is a broad inquiry into the framework's goals. The second question specifically asks about AI transparency according to NIST, which is a narrower aspect of the framework. Thus, they differ in depth and breadth.", 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What role do algorithmic impact assessments play in clarifying accountability for automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential risks associated with confabulated content in generative AI systems. It is clear in specifying the topic of interest (confabulated content in generative AI systems) and seeks detailed information on the associated risks. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the potential risks associated with confabulated content in generative AI systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What criteria should automated systems meet to ensure both safety and the prevention of algorithmic discrimination, and how should these be independently evaluated and reported?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems in decision-making have been associated with issues such as reflecting and reproducing existing unwanted inequities, embedding new harmful bias and discrimination, and being unsafe or ineffective in areas like patient care, hiring, and credit decisions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of incident response plans in managing GAI (General Artificial Intelligence) system incidents. It is clear in specifying the topic of interest (incident response plans) and the context (GAI system incidents), making the intent straightforward and understandable. The question is self-contained and does not rely on external references or additional context, making it specific, independent, and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of incident response plans in managing GAI system incidents?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Executive Order 13960', 'Trustworthy Artificial Intelligence', 'AI Bill of Rights', 'NIST AI Risk Management Framework', 'Stakeholder engagement']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what users should be notified about regarding automated systems that impact them. It is clear in its intent, seeking specific information on user notifications related to automated systems. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What should users be notified about regarding automated systems that impact them?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the NIST AI Risk Management Framework?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the main principles outlined in the AI Bill of Rights and how they aim to protect the rights of the American public. It is clear in specifying the document of interest (AI Bill of Rights) and seeks detailed information on both the principles and their protective measures. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does prompt human intervention play in ensuring equitable outcomes and effective fallback mechanisms within automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not provide specific links between tech protections and the AI Bill of Rights.', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the expectations for automated systems, while the second question focuses on clarifying accountability for automated systems. These are different aspects, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the multifaceted risks associated with prompt injection attacks on GAI (Generative AI) systems, specifically focusing on misinformation dissemination and data poisoning. It is clear in its intent, specifying the type of attack (prompt injection) and the particular risks of interest (misinformation dissemination and data poisoning). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Surveillance oversight', 'Algorithmic discrimination', 'Consent practices', 'Civil liberties']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Effective oversight in automated systems for critical fields like justice and healthcare is ensured by tailoring the systems to their intended purpose, providing meaningful access for oversight, including training for individuals interacting with the system, and incorporating human consideration for adverse or high-risk decisions. Additionally, reporting on human governance processes and assessing their timeliness, accessibility, outcomes, and effectiveness should be made public whenever possible.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Actor', 'GAI risks', 'Suggested actions', 'AI RMF functions', 'Govern 1.1']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What implications arise from the erroneous yet confident outputs of generative AI, particularly in relation to the dissemination of dangerous content and the potential for misleading users in critical decision-making scenarios?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What risks do prompt injection attacks pose to GAI, especially regarding misinformation and data poisoning?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key elements of consent practices that should be followed to prevent abusive surveillance?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the role of AI actors in the AI system lifecycle?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What roles do incident response plans play in evaluating GAI system performance and ensuring effective communication among AI Actors during incidents?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "AI nudification technology addresses the problem of creating non-consensual intimate images that can lead to image-based abuse, particularly impacting women. This technology raises wider concerns about automated harm as it exemplifies how advanced tools can be misused, leading to devastating effects on victims' personal and professional lives, as well as their mental and physical health. Additionally, the reliance on automated systems can result in unintended consequences, such as incorrect penalization of drivers or biased decision-making based on flawed historical data, highlighting the need for safeguards and ethical reviews in technology deployment.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Accessibility standards', 'Disparity assessment', 'Algorithmic discrimination', 'Ongoing monitoring and mitigation']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Organizational responsibilities', 'Incident monitoring', 'Document retention policy', 'AI system inventory']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the criteria that automated systems should meet to ensure safety and prevent algorithmic discrimination, as well as how these criteria should be independently evaluated and reported. It is clear in its intent, specifying the dual focus on safety and discrimination prevention, and seeks detailed information on both the criteria and the evaluation/reporting process. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the NIST AI Risk Management Framework. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific framework.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What standards should automated systems follow for safety and fairness, and how to assess them?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to address algorithmic discrimination in automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What actions are suggested for managing the AI system inventory according to organizational risk priorities?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address the impact of prompt injection attacks on GAI systems, the second question specifically focuses on misinformation and data poisoning, adding additional constraints and depth.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key information that users must receive about automated systems affecting their outcomes. It is clear in its intent, seeking specific details about the necessary information users should be provided with. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The main principles outlined in the AI Bill of Rights are not explicitly listed in the provided context. However, the context discusses the Blueprint for an AI Bill of Rights, which consists of five principles aimed at guiding the design, use, and deployment of automated systems to protect the rights of the American public. It emphasizes the importance of technical protections and practices to guard against potential harms and outlines expectations for automated systems, including transparency and reporting.', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key info must users receive about automated systems affecting their outcomes?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Continuous monitoring of GAI system impacts', 'Harmful bias and homogenization', 'Structured human feedback exercises', 'GAI red-teaming', 'Information integrity']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of erroneous yet confident outputs from generative AI, specifically in the context of disseminating dangerous content and misleading users in critical decision-making scenarios. It is clear in its intent, specifying the type of AI output (erroneous yet confident) and the contexts of interest (dangerous content dissemination and critical decision-making). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of prompt human intervention in ensuring equitable outcomes and effective fallback mechanisms within automated systems. It is clear in specifying the topic of interest (human intervention, equitable outcomes, fallback mechanisms, automated systems) and seeks detailed information on the impact and importance of human intervention. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key elements of consent practices that should be followed to prevent abusive surveillance. It is clear in specifying the topic of interest (consent practices) and the context (preventing abusive surveillance). The intent is straightforward, seeking specific elements or guidelines. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of consent practices and surveillance. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the key elements of consent practices that should be followed to prevent abusive surveillance?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of AI actors in the AI system lifecycle. It is clear in specifying the topic of interest (AI actors) and the context (AI system lifecycle). The intent is to understand the specific roles or functions these actors play within the lifecycle of an AI system. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Surveillance oversight', 'Algorithmic discrimination', 'Consent practices', 'Civil liberties']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Preventable harms', 'Ethics review', 'Sepsis prediction model', 'Algorithmic bias']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of continuous monitoring of GAI system impacts?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on expectations for safety and effectiveness, while the second question addresses standards for safety and fairness and how to assess them. The second question has a broader scope and different requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the roles of incident response plans in evaluating GAI (General Artificial Intelligence) system performance and ensuring effective communication among AI Actors during incidents. It is clear in specifying the topic of interest (incident response plans, GAI system performance, communication among AI Actors) and seeks detailed information on both evaluation and communication aspects. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What are the risks of confident but wrong outputs from generative AI?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do civil rights play in the context of automated systems and technology according to the foreword?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to address algorithmic discrimination in automated systems. It is clear in its intent, seeking specific actions or strategies to mitigate discrimination caused by algorithms. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What measures should be taken to address algorithmic discrimination in automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What were the shortcomings of the sepsis prediction model implemented in hospitals?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do civil liberties play in the context of surveillance systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does human input affect fairness and fallback in automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What's the role of incident response plans in assessing GAI performance and AI Actor communication during incidents?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions for managing the AI system inventory based on organizational risk priorities. It is clear in its intent, seeking specific actions related to risk management in the context of AI systems. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable with sufficient domain knowledge.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What actions are suggested for managing the AI system inventory according to organizational risk priorities?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Prompt injection attacks pose significant risks to GAI by enabling attackers to modify inputs to the system, leading to unintended behaviors and potential misinformation. Direct prompt injections can result in malicious prompts being inputted, causing negative consequences for interconnected systems. Indirect prompt injection attacks exploit vulnerabilities in LLM-integrated applications, potentially leading to the theft of proprietary data or the execution of malicious code. Additionally, data poisoning is a risk where adversaries compromise training datasets, manipulating the outputs or operations of GAI systems, which can exacerbate misinformation and the reliability of generated content.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the risks associated with incorrect outputs from generative AI systems, requiring a similar level of detail and scope in the response.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What specific consent practices and design principles should be implemented in automated systems to effectively mitigate the risks of abusive surveillance while ensuring user privacy and control over their data?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about discussions related to positive use cases during meetings conducted by OSTP. It is clear in specifying the topic of interest (positive use cases) and the context (meetings conducted by OSTP). However, it assumes familiarity with the specific meetings and their content without providing additional context or details about which meetings or time frame are being referred to. To improve clarity and answerability, the question could specify the particular meetings or time period of interest, or provide more context about the discussions being referred to.', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'AI actors play an active role in the AI system lifecycle, including organizations and individuals that deploy or operate AI.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the NIST AI Risk Management Framework is to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. It aims to foster the development of innovative approaches to address characteristics of trustworthiness including accuracy, explainability and interpretability, reliability, privacy, robustness, safety, security (resilience), and mitigation of unintended and/or harmful bias, as well as of harmful uses.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions ask for the same information regarding user notifications about automated systems that impact them, sharing the same depth, breadth, and requirements for the answer.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What proactive and ongoing strategies should be implemented in the design and assessment of automated systems to prevent algorithmic discrimination, particularly concerning underserved communities?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of continuous monitoring of GAI (General Artificial Intelligence) system impacts. It is clear in specifying the topic of interest (continuous monitoring of GAI system impacts) and seeks an explanation of the purpose behind this practice. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of continuous monitoring of GAI system impacts?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the importance of timely human consideration, while the second question is more specific about how human input affects fairness and fallback. They have different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems should follow standards that include independent evaluation to ensure safety and effectiveness, regular reporting on system performance and data usage, and protections against algorithmic discrimination. Assessments should involve algorithmic impact assessments that detail consultation results, equity assessments, and any disparities, with findings made public whenever possible.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of incident response plans specifically in managing GAI system incidents, while the second question addresses the role of these plans in assessing GAI performance and AI Actor communication during incidents. The second question has a broader scope and different requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What recommended strategies should be implemented for the oversight and inventory management of GAI systems, considering both organizational risk priorities and the lifecycle impacts of AI technology?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of civil liberties in the context of surveillance systems. It is clear in specifying the topic of interest (civil liberties) and the context (surveillance systems), making the intent clear and understandable. The question is self-contained and does not rely on external references or prior knowledge, making it independent and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Algorithmic discrimination', 'Independent evaluation', 'Algorithmic impact assessment', 'Public accountability']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Pre-deployment testing', 'GAI system validity', 'Measurement gaps', 'Structured public feedback', 'AI Red-teaming']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data broker exposes social media profiles', 'Facial recognition technology', 'Surveillance technology', 'Virtual testing and disabled students', 'New surveillance technologies and disability discrimination']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the shortcomings of a sepsis prediction model implemented in hospitals. It is clear in its intent, seeking information on the limitations or issues associated with the model. However, it lacks specificity regarding which sepsis prediction model is being referred to, as there could be multiple models with different implementations. To improve clarity and answerability, the question could specify the particular model or provide additional context about the implementation (e.g., the name of the model, the hospital or study involved).', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What were the shortcomings of the sepsis prediction model implemented in hospitals?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of civil rights in the context of automated systems and technology, specifically according to 'the foreword'. While it is clear in its intent to understand the perspective provided in the foreword, it assumes access to and familiarity with this specific foreword without providing its content or context. This makes the question unclear for those who do not have access to the foreword. To improve clarity and answerability, the question could include a brief summary or key points from the foreword, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What role do civil rights play in the context of automated systems and technology according to the foreword?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Trustworthy AI', 'Transparency policies', 'Risk management activities', 'Information integrity', 'GAI capabilities']
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for automated systems regarding algorithmic discrimination and reporting?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the limitations of current pre-deployment testing approaches for GAI applications?"
[ragas.testset.evolutions.INFO] seed question generated: "What impact do new surveillance technologies have on disability discrimination?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for particular consent practices and design principles to mitigate risks of abusive surveillance in automated systems while ensuring user privacy and control over their data. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking actionable and detailed information on consent practices and design principles.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Civil rights and liberties', 'Public consultation', 'Algorithmic harms']
[ragas.testset.evolutions.INFO] seed question generated: "What factors should be considered to ensure information integrity in the context of GAI risk management?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does human-AI integration play in enhancing customer service?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What consent practices and design principles can help balance user privacy and surveillance risks in automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Automated systems', 'Timely human consideration', 'Fallback and escalation process', 'Sensitive domains']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for proactive and ongoing strategies to prevent algorithmic discrimination in automated systems, with a particular focus on underserved communities. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking strategies for design and assessment to address a specific issue.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What are the implications of ongoing evaluations of GAI system effects on equitable content generation and community feedback integration?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do algorithmic harms play in shaping the principles of the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies can help prevent algorithmic bias in automated systems for underserved communities?"
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Incident response plans play a crucial role in assessing GAI performance by providing structured procedures for addressing the generation of inappropriate or harmful content. They ensure that incidents are communicated to relevant AI Actors, including affected communities, and that processes for tracking, responding to, and recovering from incidents are followed and documented. This structured approach helps in understanding the root causes of incidents and implementing preventive measures, thereby enhancing overall AI Actor communication during such events.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not explicitly address how human input affects fairness and fallback in automated systems. However, it mentions that human consideration and fallback mechanisms should be proportionate, accessible, equitable, timely, and effective, which implies that human input is crucial in ensuring these aspects are upheld. The effectiveness of human involvement is emphasized through training, assessment, and oversight to combat automation bias and ensure appropriate results.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for recommended strategies for the oversight and inventory management of GAI (General Artificial Intelligence) systems, considering organizational risk priorities and the lifecycle impacts of AI technology. It is clear in specifying the topic of interest (oversight and inventory management of GAI systems) and the factors to consider (organizational risk priorities and lifecycle impacts). The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Civil liberties play a crucial role in the context of surveillance systems by ensuring that civil rights are not limited by the threat of surveillance or harassment facilitated by automated systems. Surveillance systems should not monitor the exercise of democratic rights, such as voting, privacy, peaceful assembly, speech, or association, in a way that restricts these civil liberties. Additionally, information related to identity should be carefully limited to avoid algorithmic discrimination, and continuous surveillance should not be used in ways that suppress the exercise of rights.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for automated systems in terms of algorithmic discrimination and reporting. It is clear in its intent, seeking information on both discrimination and reporting aspects. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the expectations for automated systems regarding algorithmic discrimination and reporting?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the impact of new surveillance technologies on disability discrimination. It is clear in its intent, seeking information on the relationship between surveillance technologies and disability discrimination. The question is specific and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What impact do new surveillance technologies have on disability discrimination?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies are best for managing GAI systems and their lifecycle risks?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the limitations of current pre-deployment testing approaches for GAI (General Artificial Intelligence) applications. It is specific in its focus on pre-deployment testing and GAI applications, and it clearly seeks information about the limitations of these approaches. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses specifically on key elements of consent practices to prevent abusive surveillance, while the second question has a broader scope, including both consent practices and design principles to balance user privacy and surveillance risks. This difference in scope and depth leads to different requirements for the answers.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the shortcomings of a sepsis prediction model implemented in hospitals. It is clear in its intent, seeking information on the limitations or issues associated with the model. However, it lacks specificity regarding which sepsis prediction model is being referred to, as there could be multiple models with different implementations. To improve clarity and answerability, the question could specify the particular model or provide additional context about the implementation (e.g., the name of the model, the hospital or study involved).', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI technology mapping', 'Legal risks', 'Data privacy', 'Intellectual property', 'Harmful biases']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'AI Bill of Rights', 'Civil rights and liberties', 'Equal opportunities', 'Access to critical resources']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of civil rights in the context of automated systems and technology, specifically according to 'the foreword'. While it is clear in specifying the topic of interest (civil rights, automated systems, technology) and the source of information (the foreword), it assumes access to and understanding of 'the foreword' without providing its content or context. This makes the question unclear for those without direct access to the foreword. To improve clarity and answerability, the question could include a brief description or key points from the foreword, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of human-AI integration in enhancing customer service. It is clear in its intent, seeking information on how the combination of human and AI efforts can improve customer service. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role does human-AI integration play in enhancing customer service?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about factors to consider for ensuring information integrity in the context of GAI (General Artificial Intelligence) risk management. It is clear in specifying the topic of interest (information integrity) and the context (GAI risk management). The intent is also clear, seeking a list or discussion of relevant factors. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What factors should be considered to ensure information integrity in the context of GAI risk management?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address algorithmic issues in automated systems, the first question is broader, asking about measures to address discrimination in general, whereas the second question specifically focuses on strategies to prevent bias in underserved communities, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Trustworthy AI', 'Transparency policies', 'Risk management activities', 'Information integrity', 'GAI capabilities']
[ragas.testset.evolutions.INFO] seed question generated: "What are the suggested actions for addressing legal risks associated with AI technology?"
[ragas.testset.evolutions.INFO] seed question generated: "What types of automated systems should be covered by the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for using automated systems in sensitive domains. It is clear in its intent, seeking information on factors to consider, and does not rely on external references or unspecified contexts. The question is specific and independent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.evolutions.INFO] seed question generated: "What characteristics are integrated into organizational policies to ensure trustworthy AI?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the influence of algorithmic harms on the principles of the Blueprint for an AI Bill of Rights. It is clear in specifying the topic of interest (algorithmic harms) and the document in question (Blueprint for an AI Bill of Rights). The intent is to understand the relationship between these harms and the principles outlined in the Blueprint. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-generated content', 'Real-time auditing tools', 'User feedback mechanisms', 'Synthetic data', 'Incident response and recovery plans']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the implications of ongoing evaluations of GAI (Generative AI) system effects on equitable content generation and community feedback integration. It is clear in specifying the topic of interest (GAI system evaluations) and the aspects to be considered (equitable content generation and community feedback integration). However, the question could benefit from more specificity regarding what is meant by 'ongoing evaluations' and the context in which these evaluations are taking place. Providing a brief description or example of these evaluations would make the question more self-contained and easier to answer comprehensively.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are the implications of ongoing evaluations of GAI system effects on equitable content generation and community feedback integration?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What proactive measures and reporting requirements should automated systems implement to prevent algorithmic discrimination and ensure equitable outcomes for marginalized communities?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What are the implications of emerging surveillance tech on the discrimination faced by individuals with disabilities in various sectors?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on actions for managing AI system inventory based on organizational risk priorities, while the second question is broader, asking for strategies to manage GAI systems and their lifecycle risks. The depth and breadth of the inquiries differ.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.evolutions.INFO] seed question generated: "What procedures should be developed and updated in incident response and recovery plans for GAI systems when a previously unknown risk is identified?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of human-AI configuration in ensuring the adequacy of GAI system user instructions?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to address legal risks associated with AI technology. It is clear in its intent, specifying the focus on legal risks and the need for actionable suggestions. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the suggested actions for addressing legal risks associated with AI technology?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the benefits of combining AI tools with human agents in customer service. It is clear in its intent, seeking information on the advantages of this combination. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not provide specific strategies for managing GAI systems and their lifecycle risks.', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Consent practices that can help balance user privacy and surveillance risks in automated systems include use-specific consent, where consent is sought for specific, narrow use contexts and time durations, and should be re-acquired if conditions change. Additionally, brief and direct consent requests should be used, employing short, plain language to ensure users understand the context and duration of data use. User experience research should be conducted to ensure these requests are accessible and comprehensible, avoiding manipulative design choices. Furthermore, privacy should be protected by design and by default, with privacy risks assessed throughout the development life cycle and data collection minimized to only what is necessary for identified goals.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the characteristics integrated into organizational policies to ensure trustworthy AI. It is clear in its intent, seeking specific information about the elements that contribute to trustworthy AI within organizational policies. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What characteristics are integrated into organizational policies to ensure trustworthy AI?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Strategies to prevent algorithmic bias in automated systems for underserved communities include conducting proactive equity assessments during the design phase, ensuring the use of representative and robust data, and guarding against the use of proxies that may lead to algorithmic discrimination. These strategies involve reviewing potential input data, historical context, and accessibility for people with disabilities, as well as testing for correlation between demographic information and attributes to identify and remove any proxies.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the types of automated systems that should be covered by the AI Bill of Rights. It is clear in its intent, seeking specific information about the scope of the AI Bill of Rights in terms of automated systems. The question is independent and does not rely on external references or prior knowledge not included within the question itself. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for proactive measures and reporting requirements that automated systems should implement to prevent algorithmic discrimination and ensure equitable outcomes for marginalized communities. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking detailed information on both measures and reporting requirements to address algorithmic discrimination.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What benefits arise from combining AI tools with human agents in customer service?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Sensitive domains', 'Predictive analytics', 'Student data collection', 'Employee data transfer']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps should automated systems take to avoid bias and support equity for marginalized groups?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the factors influencing the choice of human alternatives over automated systems in sensitive areas. It is clear in its intent, seeking information on the reasons behind such decisions. The question is independent and does not rely on external references or unspecified contexts. However, it could benefit from specifying what is meant by 'sensitive areas' (e.g., healthcare, security, legal decisions) to provide more context and focus for the answer.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of emerging surveillance technology on the discrimination faced by individuals with disabilities across various sectors. It is clear in specifying the topic of interest (emerging surveillance tech, discrimination, individuals with disabilities) and seeks information on the implications across different sectors. The intent is clear, and the question is independent as it does not rely on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What combined strategies should be implemented to mitigate both intellectual property and privacy risks associated with the use of AI training data?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Current pre-deployment TEVV processes used for GAI applications may be inadequate, non-systematically applied, or fail to reflect or be mismatched to deployment contexts. Anecdotal testing of GAI system capabilities through video games or standardized tests designed for humans does not guarantee GAI system validity or reliability. Additionally, jailbreaking or prompt engineering tests may not systematically assess validity or reliability risks. Measurement gaps can arise from mismatches between laboratory and real-world settings, and current testing approaches often remain focused on laboratory conditions or restricted to benchmark test datasets that may not extrapolate well to real-world conditions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the implications of ongoing evaluations of GAI (Generative AI) system effects on equitable content generation and community feedback integration. It is clear in specifying the topic of interest (GAI system evaluations) and the aspects it is concerned with (equitable content generation and community feedback integration). However, the term 'ongoing evaluations' is somewhat vague and could benefit from more specificity, such as the type of evaluations or the context in which they are being conducted. Additionally, the question could be clearer by specifying what kind of implications are of interest (e.g., social, technical, ethical). Overall, the question is mostly clear but could be improved with more detail.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI', 'AI Risk Management Framework', 'Trustworthy AI', 'Bias in Artificial Intelligence', 'GPT-4 Technical Report', 'Unsafe Diffusion']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What factors influence opting for human alternatives over automated systems in sensitive areas?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What impact does new surveillance tech have on discrimination against individuals with disabilities?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of human-AI configuration in ensuring the adequacy of GAI system user instructions. It is clear in specifying the topic of interest (human-AI configuration, GAI system user instructions) and seeks information on the importance or impact of this configuration. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the procedures that should be developed and updated in incident response and recovery plans for GAI (General Artificial Intelligence) systems when a previously unknown risk is identified. It is clear in specifying the context (incident response and recovery plans for GAI systems) and the condition (when a previously unknown risk is identified). The intent is to understand the necessary procedural updates in response to new risks, making it specific and answerable without requiring additional context or external references.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the benefits and roles of integrating AI with human agents in customer service, requiring similar depth and breadth of explanation.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are some concerns related to data privacy in the context of sensitive domains?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What are the effects of GAI evaluations on fair content and community input?"
[ragas.testset.evolutions.INFO] seed question generated: "What efforts are being made to identify and manage bias in artificial intelligence according to NIST?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Information sharing and feedback mechanisms', 'AI impact assessment', 'Organizational policies', 'Third-party rights']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The types of automated systems that should be covered by the AI Bill of Rights include those that have the potential to meaningfully impact civil rights, civil liberties, or privacy, equal opportunities, and access to critical resources and services. Examples include speech-related systems, surveillance and criminal justice algorithms, voting-related systems, education-related systems, housing-related systems, employment-related systems, health technologies, and financial system algorithms.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Digital content transparency', 'Harmful bias', 'Content provenance', 'AI system trustworthiness']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on expectations for automated systems in terms of algorithmic discrimination and reporting, while the second question is more specific about steps to avoid bias and support equity for marginalized groups. They differ in both constraints and depth of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about policies that assess GAI (General Artificial Intelligence) risks while ensuring transparency and safety measures. It is clear in its intent, seeking information on specific policies related to GAI risk assessment, transparency, and safety. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Ongoing monitoring', 'Clear organizational oversight', 'High-quality data', 'Governance procedures']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of AI impact assessment in relation to feedback from individuals and communities?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 1, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Algorithmic discrimination protections', 'Equitable design', 'Independent evaluation and reporting']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the impact of new surveillance technologies on discrimination against individuals with disabilities, sharing the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of digital content transparency in relation to the societal impacts of AI?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on considerations for using automated systems in sensitive domains, while the second question asks about the factors influencing the choice between humans and automation in sensitive areas. These questions have different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for combined strategies to mitigate intellectual property and privacy risks associated with the use of AI training data. It is clear in specifying the type of risks (intellectual property and privacy) and the context (AI training data), making the intent clear and understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it independent and answerable based on domain knowledge.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for automated systems regarding ongoing monitoring and organizational oversight?"
[ragas.testset.evolutions.INFO] seed question generated: "What protections does the AI Bill of Rights provide against algorithmic discrimination?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of continuous monitoring of GAI system impacts, while the second question is about the effects of GAI evaluations on fair content and community input. These questions have different constraints and requirements, as well as different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI technologies', 'Content provenance', 'Synthetic content detection', 'Digital transparency mechanisms', 'Provenance data tracking']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The significance of human-AI configuration in ensuring the adequacy of GAI system user instructions is highlighted in the context where it mentions verifying the adequacy of GAI system user instructions through user testing. This suggests that human-AI configuration plays a crucial role in assessing and improving the effectiveness of user instructions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the efforts made by NIST to identify and manage bias in artificial intelligence. It is specific in mentioning the organization (NIST) and the topic of interest (bias in artificial intelligence), making the intent clear. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What efforts are being made to identify and manage bias in artificial intelligence according to NIST?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What policies assess GAI risks while ensuring transparency and safety measures?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies can help reduce IP and privacy risks in AI training data?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Develop and update GAI system incident response and recovery plans and procedures to address the following: Review and maintenance of policies and procedures to account for newly encountered uses; Review and maintenance of policies and procedures for detection of unanticipated uses; Verify response and recovery plans account for the GAI system value chain; Verify response and recovery plans are updated for and include necessary details to communicate with downstream GAI system Actors: Points-of-Contact (POC), Contact information, notification format.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does synthetic content detection play in managing risks associated with AI-generated outputs?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about concerns related to data privacy in sensitive domains. It is clear in its intent, seeking information on potential issues or challenges in this area. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. However, it could be improved by specifying what is meant by 'sensitive domains' (e.g., healthcare, finance) to narrow down the scope and provide a more focused answer.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data broker exposes social media profiles', 'Facial recognition technology', 'Surveillance technology', 'Virtual testing and disabled students', 'New surveillance technologies and disability discrimination']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the key elements that must be assessed to maintain information integrity amid GAI (General Artificial Intelligence) risk factors. It is clear in its intent, seeking specific elements related to information integrity and GAI risk factors. However, the acronym 'GAI' might not be universally recognized without context, and the term 'info integrity' could be more explicitly defined. To improve clarity, the question could spell out 'General Artificial Intelligence' and provide a brief explanation of what is meant by 'information integrity'.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Stakeholder communities', 'Unacceptable use', 'GAI risks', 'Information integrity', 'Governance and oversight']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems should take several steps to avoid bias and support equity for marginalized groups, including conducting proactive equity assessments during the design phase to identify potential discrimination, using representative and robust data that reflects local communities, and guarding against the use of demographic proxies that could lead to algorithmic discrimination. These steps should be integrated throughout the design, development, and deployment processes to ensure ongoing protection against algorithmic discrimination.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What challenges do virtual testing environments pose for disabled students?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key elements must be assessed to maintain info integrity amid GAI risk factors?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Executive Order 13960', 'Trustworthy Artificial Intelligence', 'AI Bill of Rights', 'NIST AI Risk Management Framework', 'Stakeholder engagement']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for automated systems in terms of ongoing monitoring and organizational oversight. It is clear in its intent, seeking information on specific aspects of automated systems (monitoring and oversight). The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the expectations for automated systems regarding ongoing monitoring and organizational oversight?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to manage GAI risks according to the provided context?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question addresses legal risks associated with AI technology in general, while the second question specifically focuses on IP and privacy risks in AI training data. These questions have different constraints and requirements.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The choice of humans over automation in sensitive areas is driven by the need for human consideration and remedy, particularly in contexts where automated systems may fail, produce errors, or where individuals wish to appeal or contest the impacts of these systems. This choice is also influenced by the requirement for appropriateness based on reasonable expectations, ensuring broad accessibility, and protecting the public from especially harmful impacts.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the protections provided by the AI Bill of Rights against algorithmic discrimination. It is specific and clear in its intent, seeking information on a particular aspect of the AI Bill of Rights. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of the AI Bill of Rights, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What protections does the AI Bill of Rights provide against algorithmic discrimination?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on characteristics integrated into organizational policies for trustworthy AI, while the second question is specifically about policies ensuring GAI risk assessment with transparency and safety. These questions have different focuses and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the purpose of AI impact assessment in relation to feedback from individuals and communities. It is clear in specifying the topic of interest (AI impact assessment) and the context (feedback from individuals and communities). The intent is to understand the role or purpose of such assessments, making it specific and answerable without needing additional context or external references. Therefore, the question meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the NIST AI Risk Management Framework?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Participatory engagement methods', 'Field testing', 'AI red-teaming', 'User feedback', 'Risk management']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of synthetic content detection in managing risks associated with AI-generated outputs. It is clear in specifying the topic of interest (synthetic content detection) and the context (managing risks associated with AI-generated outputs). The intent is clear, and the question is independent, not relying on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions ask about factors ensuring information integrity in the context of GAI risk management, sharing the same depth, breadth, and requirements for the answer.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of digital content transparency in relation to the societal impacts of AI. It is clear in specifying the topic of interest (digital content transparency) and its context (societal impacts of AI). The intent is to understand the importance or role of transparency in this specific context. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about initiatives that connect NIST's AI Safety Institute to bias management in AI. It is specific in mentioning the organization (NIST's AI Safety Institute) and the topic of interest (bias management in AI), making the intent clear. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'User consent', 'Automated systems', 'Surveillance technologies', 'Sensitive domains', 'Data protection', 'Privacy by design', 'User consent', 'Sensitive domains', 'Surveillance technologies']
[ragas.testset.evolutions.INFO] seed question generated: "What are some real-life examples of how human alternatives can be implemented in practice?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the challenges that virtual testing environments pose for disabled students. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on the difficulties faced by disabled students in virtual testing settings.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What challenges do virtual testing environments pose for disabled students?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does risk management play in the implementation of feedback activities for AI systems?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What initiatives link NIST's AI Safety Institute to bias management in AI?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for measures to manage GAI risks according to 'the provided context' without including or describing this context within the query. This makes the question unclear for those who do not have access to the unspecified context. For the question to be clear and answerable, it needs to either include the relevant context directly within the question or be framed in a way that does not require external information. Detailing specific aspects of GAI risks or the type of measures of interest could also help clarify the query.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What measures should be taken to manage GAI risks according to the provided context?"
[ragas.testset.evolutions.INFO] seed question generated: "What protections should be in place for data and inferences related to sensitive domains?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Concerns related to data privacy in sensitive domains include the lack of awareness among patients regarding the use of their medical data by insurance companies, the revelation of personal information (such as pregnancy) through targeted advertising, the monitoring of student conversations which may limit emotional expression and unfairly flag students with disabilities, the use of location data to identify individuals visiting abortion clinics, the collection of sensitive student data without parental consent, and the potential for discriminatory impacts from such data usage. Additionally, there are concerns about the accuracy of employee data transferred to third parties, which can affect job opportunities.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the dual aspects that automated systems must ensure for effective monitoring and oversight. It is clear in its intent, seeking specific aspects related to monitoring and oversight in automated systems. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Ethical use of AI systems', 'Department of Energy AI Advancement Council', 'Artificial Intelligence Ethical Principles', 'National Science Foundation research', 'Pretrial risk assessments transparency']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The policies that ensure GAI risk assessment with transparency and safety include establishing transparency policies and processes for documenting the origin and history of training data and generated data for GAI applications, as well as establishing policies to evaluate risk-relevant capabilities of GAI and the robustness of safety measures prior to deployment and on an ongoing basis.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the NIST AI Risk Management Framework. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific framework.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the NIST AI Risk Management Framework?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Strategies to reduce IP and privacy risks in AI training data include conducting periodic monitoring of AI-generated content for privacy risks, implementing processes for responding to potential intellectual property infringement claims, documenting training data curation policies, establishing policies for collection and retention of data, and conducting appropriate diligence on training data use to assess intellectual property and privacy risks.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What dual aspects must automated systems ensure for effective monitoring and oversight?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about measures to ensure equitable design and prevent algorithmic discrimination in automated systems. It is clear in its intent, seeking information on specific measures or practices. The question is independent and does not rely on external references or unspecified contexts. It is specific enough to be understood and answered by someone with domain knowledge in automated systems and algorithmic fairness.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What types of research does the National Science Foundation support to ensure the safety and effectiveness of automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What specific obstacles do digital assessment platforms create for students with disabilities, considering both the technological limitations and the implications of surveillance practices in educational settings?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The significance of digital content transparency in relation to the societal impacts of AI lies in providing input for training materials about the capabilities and limitations of GAI systems. This transparency is crucial for AI actors, professionals, and the public to understand the societal impacts of AI and the role of diverse and inclusive content generation.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What measures ensure equitable design and prevent algorithmic discrimination in automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Synthetic content detection plays a crucial role in managing risks associated with AI-generated outputs by helping to distinguish human-generated content from AI-generated synthetic content. It facilitates greater information access about both authentic and synthetic content, enabling users to better understand the trustworthiness of AI systems. Additionally, it can assist in tracing negative outcomes back to their source, improving information integrity, and upholding public trust.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question specifically asks about efforts to identify and manage bias in AI according to NIST, while the second question is more general and asks about the connection between NIST's AI Safety Institute and AI bias management. The depth and breadth of the inquiries differ.", 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of AI impact assessment in relation to feedback from individuals and communities is to collect, consider, prioritize, and integrate feedback regarding the potential individual and societal impacts related to AI risks. This process ensures that organizational policies and practices are in place to address these impacts effectively.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how 'human alternatives' can be implemented in practice. While it is clear that the question seeks practical examples, the term 'human alternatives' is vague and could refer to various concepts such as alternative energy sources, alternative dispute resolution methods, or even alternative educational approaches. To improve clarity and answerability, the question should specify what 'human alternatives' refers to, providing context or examples of the type of alternatives being inquired about.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are some real-life examples of how human alternatives can be implemented in practice?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of risk management in the implementation of feedback activities for AI systems. It is clear in specifying the topic of interest (risk management, feedback activities, AI systems) and seeks information on the relationship between these elements. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.INFO] seed question generated: "What are some real-life examples of how human alternatives can be implemented in practice?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about expectations for ongoing monitoring and organizational oversight, while the second question is more general and asks about dual aspects for effective oversight without specifying ongoing monitoring and organizational oversight.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What framework is being developed to enhance the trustworthiness of AI systems while ensuring compliance with existing laws and principles related to civil rights and privacy?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the role of the National Institute of Standards and Technology in the development of artificial intelligence?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for measures to manage GAI risks according to 'the provided context' without including or describing this context within the query. This makes the question unclear for those who do not have access to the unspecified context. For the question to be clear and answerable, it needs to either include the relevant context directly within the question or be framed in a way that does not require external information. Detailing specific aspects of GAI risks or the type of measures of interest could also help clarify the query.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the protections that should be in place for data and inferences related to sensitive domains. It is clear in its intent, seeking information on data protection measures specifically for sensitive domains. The question is independent and does not rely on external references or unspecified contexts. However, it could be improved by specifying what is meant by 'sensitive domains' (e.g., healthcare, finance) to provide more context and focus for the answer.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-enabled systems', 'Technological diffusion', 'Urban planning', 'Criminal justice system', 'Predictive policing']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about protections provided by the AI Bill of Rights against algorithmic discrimination, while the second question is broader, asking about fair design in automated systems without specifying the AI Bill of Rights or focusing solely on discrimination.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the types of research supported by the National Science Foundation (NSF) to ensure the safety and effectiveness of automated systems. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on NSF-supported research areas related to automated systems.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What types of research does the National Science Foundation support to ensure the safety and effectiveness of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Stakeholder meetings', 'Private sector and civil society', 'Positive use cases', 'Potential harms and oversight']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the concerns raised by panelists regarding the use of technology in the criminal justice system?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems should cover ongoing monitoring procedures and clear organizational oversight for effective oversight.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear in its intent, asking for specific obstacles that digital assessment platforms create for students with disabilities. It specifies two areas of interest: technological limitations and the implications of surveillance practices in educational settings. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What were some of the discussions related to positive use cases during the meetings conducted by OSTP?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Sensitive data', 'Ethical review', 'Data quality', 'Access limitations']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Supplier risk assessment framework', 'Third-party entities', 'Content provenance standards', 'GAI technology and service provider lists', 'Intellectual property and data privacy']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for the name or description of a framework being developed to enhance the trustworthiness of AI systems while ensuring compliance with existing laws and principles related to civil rights and privacy. It does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What challenges do digital assessments pose for students with disabilities?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how 'human alternatives' can be implemented in practice. While it is clear that the question seeks practical examples, the term 'human alternatives' is vague and could refer to various concepts such as alternative energy sources, alternative medicine, or even alternative dispute resolution methods. To improve clarity and answerability, the question should specify what is meant by 'human alternatives' and possibly provide a context or domain (e.g., technology, healthcare, environmental science) in which these alternatives are to be considered.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Training data use', 'Intellectual property', 'Data privacy risks', 'Content provenance', 'Generative AI (GAI) risks']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of the National Institute of Standards and Technology (NIST) in the development of artificial intelligence. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the role of the National Institute of Standards and Technology in the development of artificial intelligence?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Disinformation and misinformation', 'Generative AI models', 'Information security risks', 'Cybersecurity attacks']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of an ethical review in the context of using sensitive data?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of implementing a supplier risk assessment framework in evaluating third-party entities?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "Which framework aims to boost AI trustworthiness while upholding civil rights and privacy laws?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'AI Actors', 'Unanticipated impacts', 'Information integrity', 'Content provenance']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how 'human alternatives' can be implemented in practice. While it is clear that the question seeks practical examples, the term 'human alternatives' is vague and could refer to various concepts such as alternative energy sources, alternative dispute resolution methods, or even alternative lifestyles. To improve clarity and answerability, the question should specify what is meant by 'human alternatives' and the context in which they are to be implemented (e.g., in technology, energy, social systems).", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are some real-life examples of how human alternatives can be implemented in practice?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do GAI systems play in augmenting cybersecurity attacks?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Environmental impacts of GAI', 'Harmful bias in AI systems', 'Generative AI energy consumption', 'Disparities in model performance', 'Trustworthy AI characteristics']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the challenges faced by disabled students in virtual or digital testing environments, requiring similar depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to identify and quantify unanticipated impacts of GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 1, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Algorithmic discrimination protections', 'Equitable design', 'Independent evaluation and reporting']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Fair design in automated systems is ensured through proactive and continuous measures to protect individuals and communities from algorithmic discrimination. This includes conducting equity assessments as part of the system design, using representative data, ensuring accessibility for people with disabilities, performing pre-deployment and ongoing disparity testing and mitigation, and maintaining clear organizational oversight. Additionally, independent evaluation and reporting, including algorithmic impact assessments and disparity testing results, should be made public whenever possible to confirm these protections.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for concerns raised by panelists regarding the use of technology in the criminal justice system. It is clear in specifying the topic of interest (concerns, panelists, technology, criminal justice system) and seeks detailed information on the concerns. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the concerns raised by panelists regarding the use of technology in the criminal justice system?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about NSF programs that ensure automated systems are safe, trustworthy, and compliant with regulations. It is clear in its intent, specifying the type of programs (NSF) and the criteria (safety, trustworthiness, compliance with regulations). The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account regarding intellectual property when conducting diligence on training data use?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to ensure that automated systems are safe and effective?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What NSF programs ensure automated systems are safe, trustworthy, and compliant with regulations?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential consequences of disparities in model performance for different subgroups or languages in GAI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about the purpose of the NIST AI Risk Management Framework, while the second question is more general and could refer to any framework that aims to boost AI trustworthiness and uphold civil rights and privacy laws. They do not share the same constraints and requirements.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What contributions does the National Institute of Standards and Technology make towards ensuring the safety and trustworthiness of AI, particularly in relation to its risk management frameworks and standards?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about discussions related to positive use cases during meetings conducted by OSTP. It is clear in specifying the topic of interest (positive use cases) and the context (meetings conducted by OSTP). However, it assumes familiarity with the specific meetings and their content without providing additional context or details about which meetings or time frame are being referred to. To improve clarity and answerability, the question could specify the particular meetings or time period of interest, or provide more context about the discussions being referred to.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What were some of the discussions related to positive use cases during the meetings conducted by OSTP?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of an ethical review specifically in the context of using sensitive data. It is clear in its intent, seeking an explanation of the role and importance of ethical reviews when handling sensitive information. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of an ethical review in the context of using sensitive data?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated sentiment analyzer', 'Bias against Jews and gay people', 'Search engine results for minority groups', 'Advertisement delivery systems and stereotypes', 'Algorithmic discrimination in healthcare']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 1, 'structure': 3, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Algorithmic discrimination protections', 'Data privacy', 'Human alternatives', 'Automated systems']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of implementing a supplier risk assessment framework in evaluating third-party entities. It is clear in specifying the topic of interest (supplier risk assessment framework) and the context (evaluating third-party entities). The intent is straightforward, seeking an explanation of the purpose behind this implementation. The question is self-contained and does not rely on external references or additional context to be understood and answered.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What issues does the automated sentiment analyzer address regarding bias in online statements?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of GAI (General Artificial Intelligence) systems in augmenting cybersecurity attacks. It is clear in specifying the topic of interest (GAI systems and cybersecurity attacks) and seeks information on the specific role these systems play. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What considerations are involved in providing human alternatives in automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What ethical and operational concerns do panelists highlight regarding the integration of AI technologies in the criminal justice system, particularly in relation to community impact and democratic values?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for real-life examples of how 'human alternatives' can be implemented in practice. While it is clear that the question seeks practical examples, the term 'human alternatives' is vague and could refer to various concepts such as alternative energy sources, alternative medicine, or even alternative dispute resolution methods. To improve clarity and answerability, the question should specify what is meant by 'human alternatives' and possibly provide a context or domain (e.g., technology, healthcare, environmental science) in which these alternatives are to be considered.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to identify and quantify unanticipated impacts of GAI (General Artificial Intelligence) systems. It is clear in its intent, specifying the type of information sought (measures) and the context (unanticipated impacts of GAI systems). The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What measures are suggested to identify and quantify unanticipated impacts of GAI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the types of research supported by the NSF, focusing on safety and effectiveness of automated systems, while the second question asks specifically about NSF programs ensuring safety and compliance. The scope and requirements differ.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Enhanced protections and restrictions for data and inferences related to sensitive domains, including health, work, education, criminal justice, and finance, and for data pertaining to youth should put you first. In sensitive domains, your data and related inferences should only be used for necessary functions, and you should be protected by ethical review and use prohibitions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations related to intellectual property when conducting diligence on training data use. It is clear in its intent, seeking specific information on intellectual property considerations in the context of training data diligence. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What considerations should be taken into account regarding intellectual property when conducting diligence on training data use?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The NIST AI Risk Management Framework aims to boost AI trustworthiness while upholding civil rights and privacy laws.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of implementing a supplier risk assessment framework in evaluating third-party entities is to assess and monitor their performance and adherence to content provenance standards, detect anomalies and unauthorized changes, manage services acquisition and value chain risks, and ensure legal compliance.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure the safety and effectiveness of automated systems. It is clear in its intent, seeking specific actions or strategies to achieve these goals. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided. However, it could be improved by specifying the type of automated systems (e.g., industrial robots, AI software) to narrow down the scope and provide more targeted answers.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What measures should be taken to ensure that automated systems are safe and effective?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about discussions related to positive use cases during meetings conducted by OSTP. It is clear in specifying the topic of interest (positive use cases) and the context (meetings conducted by OSTP). However, it assumes familiarity with the specific meetings and their content without providing additional context or details about which meetings or time frame are being referred to. To improve clarity and answerability, the question could specify the particular meetings or time period of interest, or provide more context about the discussions being referred to.', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question is clear and specific, asking about the contributions of the National Institute of Standards and Technology (NIST) towards ensuring the safety and trustworthiness of AI, with a particular focus on its risk management frameworks and standards. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on NIST's role and efforts in AI safety and trustworthiness.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential consequences of disparities in model performance for different subgroups or languages in GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (disparities in model performance) and the context (different subgroups or languages in GAI systems). The intent is also clear, seeking information on the consequences of these disparities. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the potential consequences of disparities in model performance for different subgroups or languages in GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Ongoing monitoring', 'Clear organizational oversight', 'High-quality data', 'Governance procedures']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the considerations involved in providing human alternatives in automated systems. It is clear in its intent, seeking information on the factors to consider when integrating human alternatives into automated systems. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What considerations are involved in providing human alternatives in automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What role does NIST play in AI safety and risk management?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Homogenized outputs', 'Model collapse', 'Trustworthy AI Characteristics', 'Automation bias', 'Information integrity']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the ethical and operational concerns highlighted by panelists regarding the integration of AI technologies in the criminal justice system, with a particular focus on community impact and democratic values. It does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issues that an automated sentiment analyzer addresses concerning bias in online statements. It is clear in specifying the tool of interest (automated sentiment analyzer) and the specific aspect (bias in online statements). The intent is clear, seeking information on the problems related to bias that the tool aims to solve. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What issues does the automated sentiment analyzer address regarding bias in online statements?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What strategies are recommended for engaging with AI Actors to effectively identify and measure unforeseen consequences of GAI systems while ensuring the integrity and authenticity of AI-generated content?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'GAI systems may augment cybersecurity attacks by advancing offensive cyber capabilities such as hacking, malware, and phishing. Reports indicate that large language models (LLMs) can discover vulnerabilities in systems and write code to exploit them. Sophisticated threat actors might develop GAI-powered security co-pilots to inform attackers on how to evade threat detection and escalate privileges after gaining system access.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human subject protection', 'Content provenance', 'Data privacy', 'AI system performance', 'Anonymization techniques']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Reporting expectations', 'Transparency', 'Artificial Intelligence ethics', 'Traffic calming measures', 'AI Risk Management Framework']
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for automated systems regarding ongoing monitoring and organizational oversight?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What factors must be evaluated regarding IP and data integrity when assessing the use of training data in AI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI system evaluation', 'Safety risks', 'Harmful bias', 'Data privacy violations', 'GAI system outputs']
[ragas.testset.evolutions.INFO] seed question generated: "What can lead to model collapse in AI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what must be reviewed to ensure the ethical use of sensitive data affecting rights. It is clear in its intent, seeking information on the review process or criteria for ethical use of sensitive data. The question is independent and does not rely on external references or unspecified contexts. However, it could be improved by specifying the type of sensitive data or the context in which the ethical review is being conducted (e.g., medical data, financial data, research data). Overall, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What concerns do panelists raise about AI in criminal justice and its effects on communities and democracy?"
[ragas.testset.evolutions.INFO] seed question generated: "What criteria are used to measure AI system performance or assurance in deployment settings?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The NSF programs that ensure automated system safety and compliance include the National AI Research Institutes, which support research on safe, trustworthy, fair, and explainable AI algorithms and systems; the Cyber Physical Systems program, which supports research on developing safe autonomous and cyber physical systems with AI components; the Secure and Trustworthy Cyberspace program, which supports research on cybersecurity and privacy enhancing technologies in automated systems; the Formal Methods in the Field program, which supports research on rigorous formal verification and analysis of automated systems and machine learning; and the Designing Accountable Software Systems program, which supports research on rigorous and reproducible methodologies for developing software systems with legal and regulatory compliance in mind.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology?"
[ragas.testset.evolutions.INFO] seed question generated: "What steps are suggested to assess harmful bias in the AI system's training data?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What must be reviewed to ensure ethical use of sensitive data affecting rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What factors must be evaluated when integrating human options within automated frameworks as outlined in the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Cyberattacks', 'Intellectual Property', 'Obscene and abusive content', 'CBRN weapons', 'Chemical and biological design tools']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question focuses on the overall role of NIST in the development of AI, while the second question specifically targets NIST's involvement in AI safety and risk management. These inquiries have different depths and breadths.", 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the steps to ensure that automated systems are safe, effective, and free from algorithmic discrimination. It is clear in its intent, seeking specific measures or practices to achieve these goals. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What biases does the automated sentiment analyzer reveal in online expressions, and how do these biases compare to those found in predictive policing systems regarding transparency and accountability?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What steps ensure automated systems are safe, effective, and free from algorithmic discrimination?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with the production and access to obscene and abusive content?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question broadly addresses concerns about technology in the criminal justice system, while the second question specifically focuses on AI and its effects on communities and democracy, indicating a different depth and breadth of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for automated systems in terms of ongoing monitoring and organizational oversight. It is clear in its intent, seeking information on specific aspects of automated systems (monitoring and oversight). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the expectations for automated systems regarding ongoing monitoring and organizational oversight?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors that need to be evaluated concerning IP (Intellectual Property) and data integrity when assessing the use of training data in AI systems. It is clear in its intent, specifying the two main areas of interest (IP and data integrity) and the context (use of training data in AI systems). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for recommended strategies for engaging with AI Actors to identify and measure unforeseen consequences of GAI (General Artificial Intelligence) systems while ensuring the integrity and authenticity of AI-generated content. It is clear in its intent, specifying the need for strategies and the dual focus on unforeseen consequences and content integrity. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about issues arising from GAI (General Artificial Intelligence) model performance differences across languages and subgroups. It is clear in its intent, seeking information on the problems or challenges associated with these performance disparities. The question is independent and does not rely on external references or unspecified contexts. However, it could be improved by specifying what kind of issues are of interest (e.g., ethical, technical, social) to provide a more focused answer.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the steps suggested to assess harmful bias in an AI system's training data. It is clear in its intent, seeking specific steps or methods for bias assessment. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What steps are suggested to assess harmful bias in the AI system's training data?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors that can lead to model collapse in AI systems. It is clear in its intent, seeking information on potential causes of model collapse. The question is independent and does not rely on external references or unspecified contexts. Therefore, it is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What can lead to model collapse in AI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question asks for the purpose of an ethical review, while the second question asks what to review for ethical use. The first focuses on the 'why' and the second on the 'what', leading to different depths and requirements.", 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the criteria used to measure AI system performance or assurance in deployment settings. It is clear in specifying the topic of interest (criteria for measuring AI system performance or assurance) and the context (deployment settings). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for automated systems regarding safety and effectiveness?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What to consider for IP and data integrity in AI training data?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies help engage AI Actors to assess GAI impacts while maintaining AI content integrity?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What issues arise from GAI model performance differences across languages and subgroups?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology (NIST). It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge beyond what is stated, and it clearly seeks information about the purpose of a specific framework from a specific organization.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the AI Risk Management Framework as described by the National Institute of Standards and Technology?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for measures to ensure safety and effectiveness, while the second question asks what ensures safety and fairness. The difference in focus (effectiveness vs. fairness) leads to different depths and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors to be evaluated when integrating human options within automated frameworks, specifically referencing the AI Bill of Rights. It is clear in its intent to understand the evaluation criteria and the context of the AI Bill of Rights. However, it assumes familiarity with the AI Bill of Rights without providing any details or context about it. To improve clarity and answerability, the question could briefly describe what the AI Bill of Rights entails or specify the particular aspects of it that are relevant to the integration of human options in automated frameworks.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What factors must be evaluated when integrating human options within automated frameworks as outlined in the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'NIST plays a significant role in AI safety and risk management by developing measurements, technology, tools, and standards to advance reliable, safe, transparent, explainable, privacy-enhanced, and fair artificial intelligence. They are also helping to fulfill the 2023 Executive Order on Safe, Secure, and Trustworthy AI and have established the U.S. AI Safety Institute and the AI Safety Institute Consortium to build the necessary science for the safe and trustworthy development and use of AI.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential risks associated with the production and access to obscene and abusive content. It is clear in its intent, seeking information on the risks involved. The question is self-contained and does not rely on external references or additional context to be understood. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address considerations related to intellectual property and data integrity in the context of AI training data, requiring similar depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What governance structures and ongoing monitoring practices should be established to ensure the safety and effectiveness of automated systems while addressing public concerns?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the issue of performance disparities in GAI systems across different subgroups or languages, requiring similar depth and breadth of analysis.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on identifying and quantifying unanticipated impacts of GAI systems, while the second question is about engaging AI actors to assess GAI impacts while maintaining AI content integrity. These questions have different constraints and requirements, and they explore different aspects of the topic.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Any use of sensitive data or decision processes based in part on sensitive data that might limit rights, opportunities, or access should go through a thorough ethical review and monitoring, both in advance and by periodic review. This may involve an independent ethics committee or a similarly robust process. The ethical review may determine that data should not be used or shared for specific uses even with consent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI technology mapping', 'Legal risks', 'Data privacy', 'Intellectual property', 'Harmful biases', 'Training data use', 'Intellectual property', 'Data privacy risks', 'Content provenance', 'Generative AI (GAI) risks']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What methodologies are recommended for evaluating the presence of harmful bias in AI training data while ensuring the system's overall safety and reliability?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What factors contribute to the phenomenon of model collapse in AI systems, particularly in relation to the reliance on synthetic data and the potential for harmful biases?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What overarching goals does the NIST AI Risk Management Framework aim to achieve in promoting safe and equitable AI practices, particularly in relation to public transparency and ethical standards?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for automated systems in terms of safety and effectiveness. It is clear in its intent, seeking information on the standards or criteria that automated systems should meet regarding these two aspects. The question is independent and does not rely on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Panelists raised concerns about the validity of AI systems used in the criminal justice system, noting that adverse or irrelevant data can lead to a replication of unjust outcomes. They highlighted issues such as confirmation bias and the tendency to defer to potentially inaccurate automated systems. The impact of these systems on individuals and communities is seen as potentially severe, with concerns that they lack individualization, work against the belief that people can change for the better, and can lead to job loss and custody issues. Additionally, surveillance can create chilling effects for communities and send negative signals about how they are viewed. Panelists emphasized that while transparency is important, it is not sufficient for achieving accountability, and they discussed the need for regulation that includes limits on the type and cost of such technologies.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Lisa Feldman Barrett', 'Microsoft Corporation', 'National Association for the Advancement of Colored People', 'University of Michigan Ann Arbor', 'OSTP listening sessions']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 3, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Incident Database', 'Generative AI security flaws', 'Large Language Models', 'Ethical Tensions in Human-AI Companionship', 'Disinformation Business of Chinese Influence Operations']
[ragas.testset.evolutions.INFO] seed question generated: "What risks are associated with harmful biases in AI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Clear and accessible notice', 'Explanations for decisions', 'Algorithmic impact assessments', 'User experience research']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Automated systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring to ensure they are safe and effective. They should be developed with consultation from diverse communities, stakeholders, and domain experts, and should include protective measures to prevent endangering safety. Additionally, independent evaluation and reporting that confirms the system's safety and effectiveness should be performed, with results made public whenever possible.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The potential risks associated with the production and access to obscene and abusive content include eased production of and access to obscene, degrading, and/or abusive imagery, which can cause harm. This includes synthetic child sexual abuse material (CSAM) and nonconsensual intimate images (NCII) of adults.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear in its intent, asking for governance structures and ongoing monitoring practices to ensure the safety and effectiveness of automated systems while addressing public concerns. It is specific and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Trustworthy AI', 'Transparency policies', 'Risk management activities', 'Information integrity', 'GAI capabilities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors that need to be evaluated when integrating human options within automated frameworks, specifically as outlined in the AI Bill of Rights. It is clear in its intent, specifying the context (AI Bill of Rights) and the subject of interest (factors for evaluation). The question is self-contained and does not rely on external references beyond the AI Bill of Rights, which is a well-known document in the domain. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role do algorithmic impact assessments play in the expectations for automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does the National Association for the Advancement of Colored People play in advocacy and civil rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What governance and monitoring are needed for safe, effective automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some of the challenges associated with large language models as indicated in the references?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Strategies to engage AI Actors to assess GAI impacts while maintaining AI content integrity include determining context-based measures to identify new impacts, planning regular engagements with AI Actors responsible for inputs to GAI systems, employing methods to trace the origin and modifications of digital content, integrating tools to analyze content provenance, and using structured feedback mechanisms to capture user input about AI-generated content.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of establishing transparency policies for GAI applications?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What factors to consider for human options in automated systems per the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question is clear and specific, asking for recommended methodologies to evaluate harmful bias in AI training data while ensuring the system's overall safety and reliability. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on evaluation methodologies with a focus on bias and system safety.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology in social welfare', 'Fraud detection', 'Digital ID systems', 'Healthcare access and delivery', 'Health disparities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the overarching goals of the NIST AI Risk Management Framework in promoting safe and equitable AI practices, with a particular focus on public transparency and ethical standards. It does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors contributing to model collapse in AI systems, with a specific focus on the reliance on synthetic data and the potential for harmful biases. It is clear in its intent, seeking an explanation of the causes of model collapse and the role of synthetic data and biases. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The expectations for automated systems regarding safety and effectiveness include the need for independent evaluation, where evaluators should have access to the system and associated data to perform evaluations. Additionally, entities responsible for automated systems should provide regularly-updated reports that cover an overview of the system, data used for training, risk management assessments, performance testing results, and ongoing monitoring procedures. These reports should be presented in plain language and a machine-readable format.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What methods help assess bias in AI training data while ensuring safety?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the risks associated with harmful biases in AI systems. It is clear in its intent, seeking information on the potential dangers or negative consequences of biases within AI. The question is independent and does not rely on external references or additional context to be understood. It is specific enough to be answerable by someone with domain knowledge in AI ethics or related fields.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What risks are associated with harmful biases in AI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the requirements for monitoring and oversight of automated systems, focusing on governance and ongoing monitoring. They share the same depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What goals does the NIST AI Risk Management Framework pursue for safe, equitable AI, especially in transparency and ethics?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about biases revealed by an automated sentiment analyzer in online expressions and seeks a comparison with biases in predictive policing systems, specifically regarding transparency and accountability. While the intent is clear, the question assumes familiarity with the specific biases in both systems without providing context or examples. To improve clarity and answerability, the question could specify the types of biases of interest (e.g., racial, gender), provide a brief description of the sentiment analyzer and predictive policing systems, or clarify the criteria for comparison in terms of transparency and accountability.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What biases does the automated sentiment analyzer reveal in online expressions, and how do these biases compare to those found in predictive policing systems regarding transparency and accountability?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI trustworthy characteristics', 'Human-AI Configuration', 'Information Integrity', 'Data Privacy', 'Confabulation']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What leads to model collapse in AI, especially with synthetic data and biases?"
[ragas.testset.evolutions.INFO] seed question generated: "What concerns were raised by panelists regarding healthcare access and delivery in relation to new technologies?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI incidents', 'AI Actors', 'Incident reporting', 'Documentation practices', 'AI risk management']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about considerations for human alternatives in automated systems, but the second question specifically references the AI Bill of Rights, adding a specific constraint not present in the first question.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Ethical use of AI systems', 'Department of Energy AI Advancement Council', 'Artificial Intelligence Ethical Principles', 'National Science Foundation research', 'Pretrial risk assessments transparency']
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account regarding data privacy when deploying a GAI system?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of algorithmic impact assessments in the expectations for automated systems. It is clear in specifying the topic of interest (algorithmic impact assessments) and the context (expectations for automated systems). The intent is to understand the significance or influence of these assessments on automated systems. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role do algorithmic impact assessments play in the expectations for automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does incident reporting play in improving GAI risk management across the AI ecosystem?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of establishing transparency policies for GAI (General Artificial Intelligence) applications. It is clear in its intent, seeking an explanation of the rationale behind such policies. The question is specific and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What requirements have been established regarding transparency for pretrial risk assessments in Idaho?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about challenges associated with large language models as indicated in 'the references', but it does not specify or include these references within the question itself. This makes the question unclear and dependent on external information that is not provided. To improve clarity and answerability, the question should either specify the references directly within the question or be framed in a way that does not rely on unspecified external documents. For example, it could ask about general challenges associated with large language models based on common knowledge or specific studies that are well-known in the field.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are some of the challenges associated with large language models as indicated in the references?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek methods to assess bias in AI training data, with an emphasis on safety and harmful bias. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The second question specifies additional factors such as synthetic data and biases, which introduces a broader scope and more specific constraints compared to the first question.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Safety Institute established by NIST?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions pertain to the NIST AI Risk Management Framework, the first question asks for the general purpose of the framework, whereas the second question specifically focuses on goals related to safety, equity, transparency, and ethics. This difference in focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about concerns raised by panelists regarding healthcare access and delivery in relation to new technologies. It is clear in specifying the topic of interest (concerns, healthcare access and delivery, new technologies) and seeks detailed information on the concerns raised. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What concerns were raised by panelists regarding healthcare access and delivery in relation to new technologies?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Preventable harms', 'Ethics review', 'Sepsis prediction model', 'Algorithmic bias']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Ongoing monitoring', 'Clear organizational oversight', 'High-quality data', 'Governance procedures']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of establishing transparency policies for GAI applications is to document the origin and history of training data and generated data, which advances digital content transparency while balancing the proprietary nature of training approaches.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for data privacy when deploying a GAI (General Artificial Intelligence) system. It is clear in its intent, seeking specific information on data privacy aspects related to GAI deployment. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What considerations should be taken into account regarding data privacy when deploying a GAI system?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the requirements established for transparency in pretrial risk assessments in Idaho. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What requirements have been established regarding transparency for pretrial risk assessments in Idaho?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the connections between harmful biases in AI and risks such as data privacy or misinformation. It is clear in its intent, seeking to understand the relationship between these specific issues. The question is self-contained and does not rely on external references or prior knowledge beyond general understanding of AI, biases, data privacy, and misinformation. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'White House Office of Science and Technology Policy', 'Automated systems', 'Civil rights and democratic values', 'National security and defense activities']
[ragas.testset.evolutions.INFO] seed question generated: "What are the key components of ongoing monitoring for automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of harms caused by algorithmic bias in automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of incident reporting in improving GAI (General Artificial Intelligence) risk management across the AI ecosystem. It is clear in specifying the topic of interest (incident reporting) and the context (GAI risk management within the AI ecosystem). The intent is to understand the impact or contribution of incident reporting to risk management, which is straightforward and does not rely on external references or unspecified contexts. Therefore, the question is specific, independent, and has a clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role does incident reporting play in improving GAI risk management across the AI ecosystem?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about challenges associated with large language models as indicated in 'the references', but it does not specify or include these references within the question. This makes the question dependent on external documents that are not provided, leading to ambiguity and lack of independence. To improve clarity and answerability, the question should either specify the references directly within the question or be reframed to ask about general challenges associated with large language models without relying on unspecified references.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about biases revealed by an automated sentiment analyzer in online expressions and seeks a comparison with biases in predictive policing systems, specifically regarding transparency and accountability. While the intent is clear, the question assumes familiarity with the specific biases in both systems without providing context or examples. To improve clarity and answerability, the question could specify the types of biases of interest (e.g., racial, gender), provide a brief description of the sentiment analyzer and predictive policing systems, or clarify the aspects of transparency and accountability being compared.', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 3 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Safety Institute established by NIST. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific institute.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between harmful biases in AI and risks like data privacy or misinformation?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the relationship between algorithmic impact assessments and expectations for automated system transparency. It is clear in specifying the two concepts of interest (algorithmic impact assessments and automated system transparency) and seeks to understand their connection. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Model collapse in AI can occur when model training over-relies on synthetic data, resulting in data points disappearing from the distribution of the new model's outputs. This phenomenon threatens the robustness of the model overall and can lead to homogenized outputs, amplifying any homogenization from the model used to generate the synthetic training data.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Bill of Rights as outlined by the White House Office of Science and Technology Policy?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Ongoing monitoring', 'Clear organizational oversight', 'High-quality data', 'Governance procedures']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What issues did panelists identify regarding the intersection of new technologies and healthcare access, particularly in terms of equity and community involvement?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The NIST AI Risk Management Framework aims to advance reliable, safe, transparent, explainable, privacy-enhanced, and fair artificial intelligence (AI) to realize its full commercial and societal benefits without harm to people or the planet. It also supports the development of safe, secure, and trustworthy AI, emphasizing transparency and ethical considerations in its implementation.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of the National Association for the Advancement of Colored People (NAACP) in advocacy and civil rights. It is clear in specifying the organization of interest (NAACP) and the areas of focus (advocacy and civil rights). The intent is straightforward, seeking information on the organization's functions and contributions in these areas. The question is self-contained and does not rely on external references or prior knowledge beyond a basic understanding of what the NAACP is.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does the National Association for the Advancement of Colored People play in advocacy and civil rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key components of ongoing monitoring for automated systems?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What is the link between algorithmic impact assessments and expectations for automated system transparency?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What stipulations exist in Idaho's legislation regarding the transparency and validation of pretrial risk assessments, and how do these align with federal principles for ethical AI use?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Unethical behavior', 'Text-to-image models', 'Data privacy', 'Sensitive information', 'Harmful recommendations']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['OSTP', 'Artificial intelligence', 'Biometric technologies', 'Request For Information (RFI)', 'Public comments']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Race correction in clinical algorithms', 'Algorithmic impact assessment', 'Racial equity', 'Algorithmic bias detection', 'Property appraisal and valuation equity']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the general risks associated with harmful biases in AI systems, while the second question specifically links harmful AI biases to data privacy and misinformation risks. This difference in focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key components of ongoing monitoring for automated systems. It is clear in its intent, seeking specific information about the elements involved in monitoring such systems. The question is independent and does not rely on external references or additional context to be understood. Therefore, it is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the key components of ongoing monitoring for automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for examples of harms caused by algorithmic bias in automated systems. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge and clearly seeks information on the negative impacts of algorithmic bias.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are some examples of harms caused by algorithmic bias in automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Predictive policing system', 'Gun violence risk assessment', 'Watch list transparency', 'System flaws in benefit allocation', 'Lack of explanation for decisions', 'Automated systems', 'Notice and explanation', 'Impact on lives', 'Opaque decision-making', 'Algorithmic risk assessment']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Bill of Rights as outlined by the White House Office of Science and Technology Policy. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge beyond what is stated, and it clearly seeks information about the purpose of a specific document.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the AI Bill of Rights as outlined by the White House Office of Science and Technology Policy?"
[ragas.testset.evolutions.INFO] seed question generated: "What was the purpose of the Request For Information (RFI) issued by OSTP regarding biometric technologies?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the AI Safety Institute established by NIST is to continue efforts to build the science necessary for safe, secure, and trustworthy development and use of artificial intelligence (AI), in alignment with the 2023 Executive Order on Safe, Secure, and Trustworthy AI.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the Action Plan to Advance Property Appraisal and Valuation Equity?"
[ragas.testset.evolutions.INFO] seed question generated: "What risks to privacy are associated with the use of GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of the lack of explanation for decisions made by automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the benefits of documenting GAI (General Artificial Intelligence) incidents for AI actors in the context of risk management. It is clear in its intent, seeking specific information about the advantages of such documentation. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the role of algorithmic impact assessments in setting expectations for automated systems, while the second question is concerned with the relationship between algorithmic impact assessments and transparency in automated systems. These are related but distinct inquiries with different depths and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the factors related to human knowledge and security that must be documented for GAI (General Artificial Intelligence) deployment. It is clear in specifying the topic of interest (GAI deployment) and the aspects to be documented (human knowledge and security factors). The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. However, it could be improved by specifying what is meant by 'human knowledge' in this context, as it might be interpreted in various ways (e.g., expertise, ethical considerations).", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What influence does the National Association for the Advancement of Colored People exert on civil rights advocacy, particularly in relation to the governance of emerging technologies as highlighted by recent public engagements?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issues identified by panelists concerning the intersection of new technologies and healthcare access, with a focus on equity and community involvement. It is specific in its scope (new technologies and healthcare access) and clear in its intent (issues related to equity and community involvement). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key components of ongoing monitoring for automated systems. It is clear in its intent, seeking specific information about the elements involved in monitoring such systems. The question is independent and does not rely on external references or additional context to be understood. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the key components of ongoing monitoring for automated systems?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What benefits arise from documenting GAI incidents for AI Actors in risk mgmt?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What factors related to human knowledge and security must be documented for GAI deployment?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness', 'Automated systems', 'Algorithmic discrimination', 'Independent evaluation', 'Algorithmic impact assessment', 'Public accountability']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What challenges did panelists see at the tech-healthcare equity intersection?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What ongoing procedures and stakeholder engagements are essential for ensuring the safety and effectiveness of automated systems throughout their lifecycle?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What are notable instances where automated systems have caused harm due to algorithmic bias, particularly in relation to safety violations or discriminatory impacts?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not explicitly link harmful AI biases to data privacy or misinformation risks. However, it mentions risks such as harmful biases, data privacy, and misinformation in separate sections, indicating that these issues are recognized but not directly connected in the provided text.', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the stipulations in Idaho's legislation concerning the transparency and validation of pretrial risk assessments and how these align with federal principles for ethical AI use. It is clear in specifying the topic of interest (Idaho's legislation, pretrial risk assessments, federal principles for ethical AI use) and seeks detailed information on both the state-level stipulations and their alignment with federal principles. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What precautions should be taken when using derived data sources in automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What guiding principles were established by the White House OSTP to ensure the protection of civil rights in the deployment of automated systems, and how were these principles shaped by public input?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the Request For Information (RFI) issued by the Office of Science and Technology Policy (OSTP) regarding biometric technologies. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of the RFI.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What was the purpose of the Request For Information (RFI) issued by OSTP regarding biometric technologies?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What are Idaho's rules on pretrial risk assessment transparency and their alignment with federal ethical AI standards?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the purpose of the 'Action Plan to Advance Property Appraisal and Valuation Equity'. It is specific and clear in its intent, seeking information about the objective of a particular action plan. The question is self-contained and does not rely on external references or prior knowledge beyond the name of the action plan itself, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the Action Plan to Advance Property Appraisal and Valuation Equity?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What ongoing procedures and stakeholder engagements are essential for ensuring the safety and effectiveness of automated systems throughout their lifecycle?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the risks to privacy associated with the use of GAI (General Artificial Intelligence) systems. It is specific in its focus on privacy risks and does not rely on external references or unspecified contexts. The intent is clear, seeking information on potential privacy issues related to GAI systems. Therefore, the question is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What risks to privacy are associated with the use of GAI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses specifically on data privacy considerations, while the second question encompasses a broader range of knowledge and security factors, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the role of incident reporting in improving GAI risk management across the AI ecosystem, implying a broader and more systemic inquiry. The second question asks about the perks of logging GAI incidents for AI risk management, which is narrower in scope and focuses on specific benefits.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address issues at the intersection of technology and healthcare, the first question focuses on concerns about access and delivery, whereas the second question is broader, addressing challenges at the tech-healthcare equity intersection. This difference in focus leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of the lack of explanation for decisions made by automated systems. It is clear in its intent, seeking information on the consequences or effects of this lack of explanation. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided. However, it could be improved by specifying the type of automated systems (e.g., AI, machine learning models) to narrow down the scope and provide a more focused answer.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI trustworthy characteristics', 'Human-AI Configuration', 'Information Integrity', 'Data Privacy', 'Confabulation', 'GAI systems', 'Digital content transparency', 'Harmful bias', 'Content provenance', 'AI system trustworthiness']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the influence of the National Association for the Advancement of Colored People (NAACP) on civil rights advocacy, with a specific focus on the governance of emerging technologies as highlighted by recent public engagements. It is clear in specifying the organization (NAACP) and the areas of interest (civil rights advocacy and governance of emerging technologies). However, the phrase 'recent public engagements' is somewhat vague and could benefit from more specificity. For improved clarity, the question could specify particular events, statements, or initiatives by the NAACP related to emerging technologies.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the suggested actions to address confabulation in GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the ongoing procedures and stakeholder engagements necessary to ensure the safety and effectiveness of automated systems throughout their lifecycle. It is clear in its intent, specifying the focus on safety and effectiveness and the lifecycle of automated systems. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses solely on the requirements for transparency in pretrial risk assessments in Idaho, while the second question also includes an inquiry into how these rules align with federal ethical AI standards, adding an additional layer of complexity.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for notable instances where automated systems have caused harm due to algorithmic bias, specifically focusing on safety violations or discriminatory impacts. It is clear in its intent, specifying the type of harm (safety violations or discriminatory impacts) and the cause (algorithmic bias). The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does the NAACP impact civil rights in tech governance?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What insights did OSTP aim to gather through the RFI on biometric tech, and what types of stakeholders were involved in the feedback process?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What key processes and stakeholder interactions ensure automated systems' safety and effectiveness?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What objectives does the PAVE initiative aim to achieve in relation to racial equity and the valuation disparities affecting marginalized communities?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of bias and discrimination in automated systems on the rights of the American public?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for precautions to be taken when using derived data sources in automated systems. It is clear in specifying the topic of interest (precautions, derived data sources, automated systems) and seeks detailed information on safety or best practices. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What multifaceted privacy threats arise from GAI systems, particularly concerning data usage and potential misuse by individuals?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the ongoing procedures and stakeholder engagements necessary to ensure the safety and effectiveness of automated systems throughout their lifecycle. It is clear in its intent, specifying the focus on safety and effectiveness and the lifecycle of automated systems. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the guiding principles established by the White House OSTP to ensure the protection of civil rights in the deployment of automated systems and how these principles were shaped by public input. It is clear in specifying the topic of interest (guiding principles by the White House OSTP) and seeks detailed information on both the principles and the influence of public input. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "Any examples of harm from algorithmic bias in automated systems, especially regarding safety or discrimination?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Logging GAI incidents can facilitate smoother sharing of information with relevant AI Actors, empower them in responding to and managing AI incidents, and improve GAI risk management across the AI ecosystem. It also aids in documenting and reviewing third-party inputs and plugins, which is crucial for incident disclosure.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions several knowledge and security factors for GAI deployment, including the need to document the extent of human domain knowledge employed to improve GAI system performance, verify sources and citations in GAI system outputs, track instances of anthropomorphization in GAI system interfaces, verify GAI system training data and TEVV data provenance, and regularly review security and safety guardrails, especially in novel circumstances.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What key processes and stakeholder interactions ensure automated systems' safety and effectiveness?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What principles did the White House OSTP set for civil rights in automated systems, and how was public input involved?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the components of ongoing monitoring for automated systems, while the second question addresses key processes and stakeholder interactions to ensure safety and effectiveness. These questions differ in both scope and depth.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question asks about the overall role of the NAACP in advocacy and civil rights, while the second question specifically focuses on the NAACP's impact on civil rights within the context of tech governance. These questions have different scopes and depths of inquiry.", 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Panelists identified several challenges at the tech-healthcare equity intersection, including access to and expense of broadband service, privacy concerns associated with telehealth systems, and the expense of health monitoring devices, which can exacerbate equity issues. Additionally, they discussed how racial biases and the use of race in medicine perpetuate harms and embed prior discrimination, emphasizing the need for accountability of the technologies used in medical care and the importance of hearing the voices of those subjected to these technologies.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The lack of explanation for decisions made by automated systems can lead to several implications, including the inability for individuals to understand or contest decisions that affect their lives. For instance, applicants may not know why their resumes were rejected, defendants may be unaware if their bail decisions are influenced by an automated system labeling them as 'high risk', and individuals may face difficulties in correcting errors or contesting decisions due to a lack of transparency. This opacity can result in unaccountable decision-making processes and can hinder the public's ability to trust the validity and reasonable use of automated systems.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to address confabulation in GAI (General Artificial Intelligence) systems. It is clear in specifying the issue of interest (confabulation) and the context (GAI systems), and it seeks actionable recommendations. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the insights that the OSTP aimed to gather through the RFI on biometric tech and the types of stakeholders involved in the feedback process. It is clear in specifying the topic of interest (OSTP, RFI on biometric tech) and seeks detailed information on both the insights and the stakeholders. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data broker exposes social media profiles', 'Facial recognition technology', 'Surveillance technology', 'Virtual testing and disabled students', 'New surveillance technologies and disability discrimination', 'Digital surveillance', 'Reproductive health clinics', 'Private equity firms', 'Facial recognition ban', 'User privacy protection']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the objectives of the PAVE initiative in relation to racial equity and valuation disparities affecting marginalized communities. It is clear in specifying the initiative (PAVE) and the areas of interest (racial equity and valuation disparities). The intent is clear, seeking information on the goals of the PAVE initiative. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated system', 'Plain language documentation', 'System functioning', 'Outcome explanations', 'User notification']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of bias and discrimination in automated systems on the rights of the American public. It is specific in its focus on bias and discrimination within automated systems and their impact on a particular group (the American public). The intent is clear, seeking an explanation of the consequences of these issues on rights. The question is independent and does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the components of ongoing monitoring, while the second question addresses processes and stakeholder interactions for ensuring safety and effectiveness. These questions differ in both scope and depth.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Idaho's rules on pretrial risk assessment transparency require that any pretrial risk assessment be shown to be free of bias against any class of individuals protected from discrimination by state or federal law. Additionally, any locality using a pretrial risk assessment must formally validate the claim of it being free of bias, and all documents, records, and information used to build or validate the risk assessment must be open to public inspection. However, the context does not provide specific information on how these rules align with federal ethical AI standards.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the multifaceted privacy threats arising from GAI (General Artificial Intelligence) systems, specifically focusing on data usage and potential misuse by individuals. It is clear in its intent to explore privacy threats and specifies the areas of concern (data usage and misuse). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of the AI Bill of Rights, while the second question asks about the principles set for civil rights in automated systems and the involvement of public input. These questions have different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek examples of harms caused by algorithmic bias in automated systems, with the second question specifying areas like safety or discrimination. However, the core inquiry and constraints are similar.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Opt out', 'Human alternatives', 'Automated systems', 'Human consideration', 'Sensitive domains']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What insights did OSTP seek from the biometric tech RFI, and who provided feedback?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Disinformation and misinformation', 'Generative AI models', 'Information security risks', 'Cybersecurity attacks']
[ragas.testset.evolutions.INFO] seed question generated: "What are some concerns associated with the use of surveillance technology in various sectors?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What goals does PAVE have for racial equity and valuing marginalized communities?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What should users be notified about regarding automated systems that impact them?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do GAI systems play in augmenting cybersecurity attacks?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be in place to ensure human alternatives and consideration in the use of automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Key processes and stakeholder interactions that ensure automated systems' safety and effectiveness include ongoing monitoring procedures, clear organizational oversight, consultation with the public during various phases of development, extensive testing before deployment, and proactive risk identification and mitigation. These processes involve continuous evaluation of performance metrics, involvement of organizational stakeholders, engagement with diverse impacted communities, and adherence to domain-specific best practices for testing.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What privacy risks come from GAI systems regarding data use and misuse?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Sensitive data', 'Sensitive domains', 'Surveillance technology', 'Underserved communities']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Biometric Information Privacy Act', 'Transparency for machine learning systems', 'Adverse action notices', 'Explainable AI systems', 'California warehouse employee quotas']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses solely on the purpose of the RFI issued by OSTP regarding biometric technologies, while the second question asks for both the insights sought and the sources of feedback, indicating a broader scope.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Risk assessment', 'Explanatory mechanisms', 'Transparency in decision-making', 'Summary reporting']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The implications of bias and discrimination in automated systems on the rights of the American public include limiting opportunities, preventing access to critical resources or services, and reflecting and reproducing existing unwanted inequities. These outcomes can undermine civil rights and democratic values, which are foundational American principles.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the overall purpose of the Action Plan to Advance Property Appraisal and Valuation Equity, while the second question specifically targets the goals related to racial equity and valuing marginalized communities. These questions have different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What initiatives are being taken to promote transparency for machine learning systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key considerations outlined in the AI Bill of Rights regarding sensitive data and underserved communities?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Precautions that should be taken when using derived data sources in automated systems include careful tracking and validation of derived data, as it is viewed as potentially high-risk and may lead to feedback loops, compounded harm, or inaccurate results. Such data should be validated against the risk of collateral consequences.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about concerns associated with the use of surveillance technology in various sectors. It is clear in its intent, seeking information on potential issues or drawbacks of surveillance technology across different fields. The question is independent and does not rely on external references or specific prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are some concerns associated with the use of surveillance technology in various sectors?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Algorithmic discrimination protections', 'Data privacy', 'Human alternatives', 'Automated systems']
[ragas.testset.evolutions.INFO] seed question generated: "What should be included in the design of explanatory mechanisms for automated systems in high-risk settings?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Key processes and stakeholder interactions that ensure automated systems' safety and effectiveness include ongoing monitoring procedures, clear organizational oversight, consultation with the public during various phases of development, extensive testing before deployment, and proactive risk identification and mitigation. These processes involve continuous evaluation of performance metrics, involvement of organizational stakeholders, engagement with diverse impacted communities, and adherence to domain-specific best practices for testing.", 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about privacy risks associated with GAI systems, focusing on data use and misuse. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 4 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about measures to ensure human alternatives and consideration in the use of automated systems. It is clear in its intent, seeking specific measures or guidelines. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of GAI (General Artificial Intelligence) systems in augmenting cybersecurity attacks. It is clear in specifying the topic of interest (GAI systems and cybersecurity attacks) and seeks information on the specific role these systems play. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what users should be notified about regarding automated systems that impact them. It is clear in its intent, seeking information on the types of notifications or disclosures that should be provided to users. The question is independent and does not rely on external references or unspecified contexts. However, it could be improved by specifying the types of automated systems (e.g., AI algorithms, automated decision-making systems) or the nature of the impact (e.g., privacy, decision outcomes) to provide more context and focus for the answer.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the key considerations regarding data privacy in the context of the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The Blueprint for an AI Bill of Rights includes five principles and associated practices to guide the design, use, and deployment of automated systems to protect the rights of the American public. It was developed through extensive consultation with the American public, which involved a year-long process of seeking and distilling input from impacted communities, industry stakeholders, technology developers, and policymakers. This public engagement included panel discussions, public listening sessions, and a formal request for information, allowing various voices to shape the principles aimed at preventing algorithmic and data-driven harms.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The suggested actions to address confabulation in GAI systems include: 1) Avoid extrapolating GAI system performance or capabilities from narrow, non-systematic, and anecdotal assessments (MS-2.5-001). 2) Review and verify sources and citations in GAI system outputs during pre-deployment risk measurement and ongoing monitoring activities (MS-2.5-003). 3) Evaluate GAI system performance in real-world scenarios to observe its behavior in practical environments and reveal issues that might not surface in controlled and optimized testing environments (MS-4.2-002).', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Synthetic training data', 'Model collapse', 'Environmental impact', 'GAI systems', 'Carbon capture programs']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Transparency artifacts', 'Explainable AI (XAI)', 'Pre-trained models', 'Harmful bias', 'Content filters']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI lifecycle', 'AI technology risks', 'Organizational practices for AI', 'Impact documentation process', 'Content provenance methodologies']
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to assess the environmental impact of AI model training and management activities?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Identity theft', 'Facial recognition system', 'Surveillance software', 'Employee discussions about union activity']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about initiatives to promote transparency in machine learning systems. It is clear in its intent, seeking information on specific actions or programs aimed at enhancing transparency in this field. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What initiatives are being taken to promote transparency for machine learning systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of reviewing transparency artifacts in the context of third-party models?"
[ragas.testset.evolutions.INFO] seed question generated: "What organizational practices are necessary for enabling AI testing and incident identification?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Public consultation', 'Testing and deployment', 'Risk identification and mitigation', 'Safety and effectiveness']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key considerations outlined in the AI Bill of Rights regarding sensitive data and underserved communities. It is clear in specifying the document of interest (AI Bill of Rights) and the specific aspects (sensitive data and underserved communities) it seeks information about. The intent is clear, and the question is independent as it does not rely on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the key considerations outlined in the AI Bill of Rights regarding sensitive data and underserved communities?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data broker exposes social media profiles', 'Facial recognition technology', 'Surveillance technology', 'Virtual testing and disabled students', 'New surveillance technologies and disability discrimination', 'Digital surveillance', 'Reproductive health clinics', 'Private equity firms', 'Facial recognition ban', 'User privacy protection']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'OSTP sought insights on the extent and variety of biometric technologies in past, current, or planned use; the domains in which these technologies are being used; the entities making use of them; current principles, practices, or policies governing their use; and the stakeholders that are, or may be, impacted by their use or regulation. Feedback was provided by 130 organizations and individuals, including Accenture, ACLU, Google, Microsoft Corporation, and many others.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for elements that should be included in the design of explanatory mechanisms for automated systems in high-risk settings. It is clear in specifying the topic (explanatory mechanisms) and the context (automated systems in high-risk settings), making the intent clear and understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it independent and answerable based on domain knowledge.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What should be included in the design of explanatory mechanisms for automated systems in high-risk settings?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of how data privacy principles aim to protect against identity theft?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some concerns associated with digital surveillance as highlighted in recent articles?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key considerations for testing and deployment of automated systems to ensure their safety and effectiveness?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key considerations regarding data privacy within the context of the AI Bill of Rights. It is clear in specifying the topic of interest (data privacy) and the context (AI Bill of Rights), making the intent straightforward. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Users should be notified about the use of automated systems, the individual or organization responsible for the system, significant use case or key functionality changes, and how and why an outcome impacting them was determined by the automated system.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'GAI systems may augment cybersecurity attacks by advancing offensive cyber capabilities such as hacking, malware, and phishing. Reports indicate that large language models (LLMs) can discover vulnerabilities in systems and write code to exploit them. Sophisticated threat actors might develop GAI-powered security co-pilots to inform attackers on how to evade threat detection and escalate privileges after gaining system access.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about issues arising from surveillance technology in sectors like education and healthcare. It is clear in specifying the sectors of interest (education and healthcare) and the general topic (issues from surveillance technology). The intent is to understand the problems or challenges associated with the use of surveillance tech in these specific sectors. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Technical protections', 'Rights of the American public', 'Implementation of principles']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Measures to ensure human alternatives and consideration in the use of automated systems include the ability to opt out from automated systems in favor of a human alternative where appropriate, access to timely human consideration and remedy through a fallback and escalation process if an automated system fails, and ensuring that human consideration and fallback are accessible, equitable, effective, and maintained. Additionally, automated systems in sensitive domains should be tailored to their purpose, provide meaningful access for oversight, include training for people interacting with the system, and incorporate human consideration for adverse or high-risk decisions.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What measures are being implemented to ensure the public is informed about the use of automated systems in decision-making processes, particularly regarding their rights and opportunities?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['OSTP', 'Artificial intelligence', 'Biometric technologies', 'Request For Information (RFI)', 'Public comments']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to assess the environmental impact of AI model training and management activities. It is specific in its focus on environmental impact and AI model training and management, making the intent clear. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What issues arise from surveillance tech in sectors like education and healthcare?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What critical factors regarding sensitive data protection and the rights of historically marginalized groups are emphasized in the AI Bill of Rights?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures are outlined in the Blueprint for an AI Bill of Rights to protect the rights of the American public?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of reviewing transparency artifacts in the context of third-party models. It is clear in specifying the topic of interest (transparency artifacts, third-party models) and seeks information on the purpose of this review. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human subject protection', 'Content provenance', 'Data privacy', 'AI system performance', 'Anonymization techniques']
[ragas.testset.evolutions.INFO] seed question generated: "What was the purpose of the Request For Information (RFI) issued by OSTP regarding biometric technologies?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What elements must be integrated into the design of automated systems in high-risk environments to ensure clear, valid, and accessible explanations of decisions made by these systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for examples of how data privacy principles protect against identity theft. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on the application of data privacy principles in the context of identity theft protection.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are some examples of how data privacy principles aim to protect against identity theft?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Accessibility standards', 'Disparity assessment', 'Algorithmic discrimination', 'Ongoing monitoring and mitigation']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI models', 'Synthetic NCII and CSAM', 'Trustworthy AI Characteristics', 'Value Chain and Component Integration', 'GAI risks management']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the organizational practices necessary for enabling AI testing and incident identification. It is clear in its intent, seeking specific practices related to AI testing and incident identification within an organization. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What organizational practices are necessary for enabling AI testing and incident identification?"
[ragas.testset.evolutions.INFO] seed question generated: "What criteria are used to measure AI system performance or assurance in deployment settings?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key considerations in testing and deploying automated systems to ensure their safety and effectiveness. It is clear in its intent, specifying the focus on safety and effectiveness, and does not rely on external references or unspecified contexts. The question is self-contained and understandable, making it specific and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken during disparity assessment of automated systems to ensure inclusivity and fairness?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about concerns associated with surveillance technology in various sectors, which is broader than the second question that focuses specifically on education and healthcare sectors.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What are the challenges associated with value chain and component integration in GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Clear and accessible notice', 'Explanations for decisions', 'Algorithmic impact assessments', 'User experience research']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the measures being implemented to inform the public about the use of automated systems in decision-making processes, with a focus on their rights and opportunities. It is clear in its intent, specifying the topic of interest (public information measures, automated systems, decision-making processes) and the specific aspects of interest (rights and opportunities). The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What instances illustrate how principles of data privacy mitigate risks associated with identity theft in the context of pervasive surveillance and data collection practices?"
[ragas.testset.evolutions.INFO] seed question generated: "What role do algorithmic impact assessments play in the expectations for automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the measures outlined in the 'Blueprint for an AI Bill of Rights' to protect the rights of the American public. It is specific in its request for information about the measures and clearly identifies the document of interest ('Blueprint for an AI Bill of Rights'). The intent is clear, and the question is self-contained, not relying on external references or prior knowledge beyond the named document. Therefore, it meets the criteria for clarity and answerability.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What measures are outlined in the Blueprint for an AI Bill of Rights to protect the rights of the American public?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What organizational strategies must be implemented to facilitate effective AI testing and incident reporting while ensuring comprehensive risk communication and feedback integration?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of reviewing transparency artifacts in the context of third-party models is to ensure information integrity, security, and effective value chain and component integration.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps are taken to inform the public about automated decision-making and their rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for the elements that need to be integrated into the design of automated systems in high-risk environments to ensure clear, valid, and accessible explanations of decisions made by these systems. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is also clear, seeking detailed information on design elements for explanation purposes in high-risk environments.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the Request For Information (RFI) issued by the Office of Science and Technology Policy (OSTP) regarding biometric technologies. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What was the purpose of the Request For Information (RFI) issued by OSTP regarding biometric technologies?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the critical factors related to sensitive data protection and the rights of historically marginalized groups as emphasized in the AI Bill of Rights. It is specific in its focus on two key areas (sensitive data protection and rights of marginalized groups) and clearly identifies the document of interest (AI Bill of Rights). The intent is clear, seeking information on particular aspects of the AI Bill of Rights. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the criteria used to measure AI system performance or assurance in deployment settings. It is clear in specifying the topic of interest (criteria for measuring AI system performance or assurance) and seeks detailed information on these criteria. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What elements ensure clear explanations in automated systems for high-risk environments?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The concerns with surveillance technology in education and healthcare include its invasive nature, potential for discrimination, and the disproportionate harm it may cause to disabled individuals. Specifically, new surveillance technologies can monitor students in ways that may violate their privacy and exacerbate existing inequalities, particularly for those with disabilities.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The suggested measures to assess the environmental impact of AI model training and management activities include: 1) Assessing safety to physical environments when deploying GAI systems, 2) Documenting anticipated environmental impacts of model development, maintenance, and deployment in product design decisions, 3) Measuring or estimating environmental impacts such as energy and water consumption for training, fine-tuning, and deploying models, and verifying trade-offs between resources used at inference time versus additional resources required at training time, and 4) Verifying the effectiveness of carbon capture or offset programs for GAI training and applications, while addressing green-washing concerns.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Key considerations for testing and deployment of automated systems to ensure their safety and effectiveness include extensive testing before deployment, following domain-specific best practices, considering the roles of human operators, mirroring real-world conditions during testing, comparing system performance with existing human-driven procedures, and identifying and mitigating potential risks proactively. Testing should include both automated and human-led testing, and decision possibilities should include the option of not deploying the system if performance does not meet standards.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to be taken during the disparity assessment of automated systems to ensure inclusivity and fairness. It is clear in specifying the topic of interest (disparity assessment, automated systems) and the desired outcome (inclusivity and fairness). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for concerns associated with digital surveillance as highlighted in recent articles. It is clear in specifying the topic of interest (concerns with digital surveillance) and the source of information (recent articles). However, it does not specify which articles or provide any context about the articles, making it somewhat vague. To improve clarity and answerability, the question could specify the type of concerns (e.g., privacy, security, ethical issues) or mention specific articles or sources if known.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are some concerns associated with digital surveillance as highlighted in recent articles?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the challenges associated with value chain and component integration in GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (value chain and component integration) and the context (GAI systems). The intent is to understand the challenges, which is straightforward and unambiguous. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What key points on data protection and marginalized rights are in the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on promoting transparency in machine learning systems, while the second question is about informing the public about automated decision-making and their rights. These are related but distinct topics, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What key principles and actionable practices are proposed in the Blueprint for an AI Bill of Rights to ensure the safeguarding of civil liberties and democratic values for the American populace?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Trustworthy AI', 'Transparency policies', 'Risk management activities', 'Information integrity', 'GAI capabilities']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What insights did OSTP aim to gather through the RFI on biometric tech, and what types of stakeholders were involved in the feedback process?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for instances that illustrate how principles of data privacy mitigate risks associated with identity theft in the context of pervasive surveillance and data collection practices. It is clear in specifying the topic of interest (data privacy principles, identity theft, pervasive surveillance, and data collection practices) and seeks specific examples or instances. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the components necessary for effective explanatory mechanisms in automated systems within high-risk settings, sharing the same constraints, requirements, depth, and breadth.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI model explanation', 'GAI risks', 'Privacy risk assessment', 'Data provenance', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about organizational strategies for effective AI testing, incident reporting, risk communication, and feedback integration. It is clear in its intent, specifying the areas of interest (AI testing, incident reporting, risk communication, feedback integration) and the desired outcome (effective facilitation). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Disinformation and misinformation', 'Generative AI models', 'Information security risks', 'Cybersecurity attacks']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of algorithmic impact assessments in the expectations for automated systems. It is clear in specifying the topic of interest (algorithmic impact assessments) and the context (expectations for automated systems). The intent is to understand the significance or function of these assessments within the given context. The question is self-contained and does not rely on external references or prior knowledge beyond general understanding of the terms used. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-enabled systems', 'Technological diffusion', 'Urban planning', 'Criminal justice system', 'Predictive policing', 'Artificial Intelligence and Democratic Values', 'Non-discriminatory technology', 'Explainable AI', 'Community participation', 'Social welfare systems']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of establishing transparency policies for GAI applications?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What examples show how data privacy principles reduce identity theft risks amid widespread surveillance?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What org strategies help with AI testing, incident reporting, and risk communication?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some suggested actions to address GAI risks in AI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions focus on the AI Bill of Rights and its key points regarding data protection and the rights of underserved or marginalized communities. They share the same constraints and requirements, as well as a similar depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with generative AI models in the context of disinformation and cybersecurity?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of technological diffusion in the context of integrating AI technologies within communities?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI system evaluation', 'Safety risks', 'Harmful bias', 'Data privacy violations', 'GAI system outputs']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'During disparity assessment of automated systems, measures should include testing using a broad set of measures to assess whether the system components produce disparities. The demographics of the assessed groups should be as inclusive as possible, covering aspects such as race, color, ethnicity, sex, religion, age, national origin, disability, and other classifications protected by law. The assessment should include demographic performance measures, overall and subgroup parity assessment, and calibration. Additionally, demographic data collected for disparity assessment should be separated from data used for the automated system, and privacy protections should be instituted.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Reporting expectations', 'Transparency', 'Artificial Intelligence ethics', 'Traffic calming measures', 'AI Risk Management Framework']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek examples of how data privacy principles protect against identity theft, with a focus on the context of widespread surveillance in the second question. However, the core inquiry and requirements remain the same.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 5 times
[ragas.testset.evolutions.INFO] seed question generated: "What steps are suggested to assess harmful bias in the AI system's training data?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Steps taken to inform the public about automated decision-making and their rights include written notice provided by private entities in Illinois regarding the use of biometric information, federal laws requiring lenders to notify consumers about adverse actions related to credit decisions, and California laws mandating that warehouse employees receive written descriptions of quotas. Additionally, major technology companies are developing frameworks for transparency in machine learning systems, and federal agencies are conducting research on explainable AI systems to ensure that the public understands how automated systems impact their rights and opportunities.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Homogenized outputs', 'Model collapse', 'Trustworthy AI Characteristics', 'Automation bias', 'Information integrity']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on organizational practices specifically for AI testing and incident identification, while the second question includes a broader scope by adding incident reporting and risk communication. This difference in scope affects the depth and breadth of the inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for key principles and actionable practices proposed in the 'Blueprint for an AI Bill of Rights' to safeguard civil liberties and democratic values for the American populace. It is specific in its request for principles and practices, and it clearly identifies the document of interest ('Blueprint for an AI Bill of Rights'). The intent is clear, seeking detailed information on how the document aims to protect civil liberties and democratic values. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the principles of artificial intelligence ethics as outlined for the intelligence community?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the insights that the OSTP aimed to gather through the RFI on biometric tech and the types of stakeholders involved in the feedback process. It is clear in specifying the topic of interest (OSTP, RFI on biometric tech) and seeks detailed information on both the insights and the stakeholders. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for concerns associated with digital surveillance as highlighted in recent articles. It is clear in specifying the topic of interest (concerns with digital surveillance) and the source of information (recent articles). However, it does not specify which articles or provide any context about the articles, making it somewhat dependent on external references. To improve clarity and answerability, the question could specify the articles or the key themes discussed in those articles, or alternatively, frame the question in a way that does not rely on unspecified external sources.', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of establishing transparency policies for GAI (General Artificial Intelligence) applications. It is clear in specifying the topic of interest (transparency policies for GAI applications) and seeks information on the rationale behind these policies. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of establishing transparency policies for GAI applications?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Challenges associated with value chain and component integration in GAI systems include the improper acquisition or vetting of third-party components such as datasets, pre-trained models, and software libraries, which can lead to diminished transparency and accountability. The scale of training data may be too large for humans to vet, and the difficulty of training foundation models can result in extensive reuse of a limited number of models. Additionally, it may be difficult to attribute issues in a system's behavior to any one of these sources, and errors in third-party GAI components can have downstream impacts on accuracy and robustness.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What can lead to model collapse in AI systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What principles in the AI Bill of Rights protect U.S. civil liberties?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to address GAI (General Artificial Intelligence) risks in AI systems. It is clear in its intent, seeking specific actions or strategies to mitigate risks associated with GAI. The question is independent and does not rely on external references or prior knowledge beyond a basic understanding of AI and GAI risks. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Sensitive data', 'Ethical review', 'Data quality', 'Access limitations']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Artificial Intelligence Decisionmaking', 'Biometric Information Privacy Act', 'Model Cards for Model Reporting', 'Adverse Action Notice Requirements', 'Explainable Artificial Intelligence (XAI)']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the potential risks associated with generative AI models in the context of disinformation and cybersecurity. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on risks, which allows for a direct and relevant response.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What insights did OSTP seek from the biometric tech RFI, and who provided feedback?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of technological diffusion in the context of integrating AI technologies within communities. It is clear in specifying the topic of interest (technological diffusion) and the context (integrating AI technologies within communities). The intent is to understand the importance or impact of this diffusion process. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI lifecycle', 'AI technology risks', 'Organizational practices for AI', 'Impact documentation process', 'Content provenance methodologies']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Algorithmic discrimination protections', 'Data privacy', 'Human alternatives', 'Automated systems']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of an ethical review in the context of using sensitive data?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of Explainable Artificial Intelligence (XAI) as referenced by DARPA?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Stakeholder meetings', 'Private sector and civil society', 'Positive use cases', 'Potential harms and oversight']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the steps suggested to assess harmful bias in an AI system's training data. It is clear in its intent, seeking specific steps or methods for bias assessment. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What steps are suggested to assess harmful bias in the AI system's training data?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key oversight functions involved in the GAI lifecycle?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role do transparency policies play in mitigating risks associated with GAI applications while ensuring compliance with legal and ethical standards?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Organizational strategies that help with AI testing, incident reporting, and risk communication include establishing policies for measuring the effectiveness of content provenance methodologies, identifying the minimum set of criteria necessary for GAI system incident reporting, and verifying information sharing and feedback mechanisms regarding any negative impact from GAI systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the protective measures or principles in the AI Bill of Rights aimed at safeguarding the rights or civil liberties of the American public, sharing the same depth, breadth, and requirements for the answer.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Information integrity', 'Human-AI configuration', 'Digital content transparency', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses solely on the purpose of the RFI issued by OSTP regarding biometric technologies, while the second question asks for both the insights sought and the sources of feedback, indicating a broader scope.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the principles of artificial intelligence ethics as outlined for the intelligence community. It is clear in specifying the topic of interest (principles of AI ethics) and the context (intelligence community). The intent is straightforward, seeking specific information about ethical guidelines. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the principles of artificial intelligence ethics as outlined for the intelligence community?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of human-AI configuration in managing GAI risks and ensuring information integrity?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors that can lead to model collapse in AI systems. It is clear in its intent, seeking information on potential causes of model collapse. The question is independent and does not rely on external references or unspecified contexts. Therefore, it is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What can lead to model collapse in AI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of automated systems mentioned in the technical companion to the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Some suggested actions to address GAI risks in AI systems include: applying and documenting ML explanation results such as analysis of embeddings, counterfactual prompts, gradient-based attributions, model compression/surrogate models, and occlusion/term reduction. Additionally, documenting GAI model details including proposed use and organizational value, assumptions and limitations, data collection methodologies, data provenance, data quality, model architecture, optimization objectives, training algorithms, RLHF approaches, fine-tuning or retrieval-augmented generation approaches, evaluation data, ethical considerations, and legal and regulatory requirements.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-generated content', 'Real-time auditing tools', 'User feedback mechanisms', 'Synthetic data', 'Incident response and recovery plans']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Fairness in Artificial Intelligence', 'Automatic signature verification', 'Ballot curing', 'Digital divide in unemployment benefits', 'Racial equity and underserved communities']
[ragas.testset.evolutions.INFO] seed question generated: "What topics were discussed regarding potential harms and oversight in the development of the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What methodologies are recommended for evaluating the presence of harmful bias in AI training data while ensuring the system's overall safety and reliability?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of an ethical review specifically in the context of using sensitive data. It is clear in its intent, seeking an explanation of the role and importance of ethical reviews when handling sensitive data. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of an ethical review in the context of using sensitive data?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Technological diffusion is significant in the context of integrating AI technologies within communities as it emphasizes the importance of thoughtful and responsible development and integration of technology. Panelists noted that examining how technological diffusion has worked in urban planning can provide lessons on balancing ownership rights, use rights, and community health, safety, and welfare, ensuring better representation of all voices, especially those traditionally marginalized by technological advances.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of Explainable Artificial Intelligence (XAI) as referenced by DARPA. It is clear in specifying the topic of interest (XAI) and the source of reference (DARPA), making the intent clear and specific. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of Explainable Artificial Intelligence (XAI) as referenced by DARPA?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What ethical guidelines for AI usage are established for the intelligence sector, and how do they align with NIST's standards for safe and transparent AI development?"
[ragas.testset.evolutions.INFO] seed question generated: "What issue does the digital divide in unemployment benefits highlight in relation to access for individuals?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of using real-time auditing tools in the context of AI-generated data?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key oversight functions involved in the GAI (General Artificial Intelligence) lifecycle. It is clear in specifying the topic of interest (oversight functions in the GAI lifecycle) and seeks detailed information on these functions. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the key oversight functions involved in the GAI lifecycle?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The potential risks associated with generative AI models in the context of disinformation include the ease of producing or disseminating false, inaccurate, or misleading content at scale, both unintentionally (misinformation) and deliberately (disinformation). GAI systems can enable malicious actors to create targeted disinformation campaigns, generate realistic deepfakes, and produce compelling imagery and propaganda. In terms of cybersecurity, GAI models may lower barriers for offensive capabilities, expand the attack surface, and assist in discovering vulnerabilities and writing exploit code, thereby augmenting cybersecurity attacks such as hacking, malware, and phishing.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of transparency policies in mitigating risks associated with GAI (General Artificial Intelligence) applications while ensuring compliance with legal and ethical standards. It is clear in specifying the topic of interest (transparency policies, GAI applications) and seeks detailed information on both risk mitigation and compliance with standards. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Preventable harms', 'Ethics review', 'Sepsis prediction model', 'Algorithmic bias']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What factors contribute to the phenomenon of model collapse in AI systems, particularly in relation to the reliance on synthetic data and the potential for harmful biases?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do transparency policies help manage GAI risks and ensure compliance?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'OSTP sought insights on the extent and variety of biometric technologies in past, current, or planned use; the domains in which these technologies are being used; the entities making use of them; current principles, practices, or policies governing their use; and the stakeholders that are, or may be, impacted by their use or regulation. Feedback was provided by 130 organizations and individuals, including Accenture, ACLU, Google, Microsoft Corporation, and many others.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What actions can be taken to prevent the harms associated with automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['TEVV metrics', 'Measurement error models', 'GAI system risks', 'Feedback processes', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of human-AI configuration in managing GAI (General Artificial Intelligence) risks and ensuring information integrity. It is clear in specifying the topic of interest (human-AI configuration) and the areas of concern (GAI risks and information integrity). The intent is clear, seeking an explanation of the importance or impact of this configuration. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does an ethical review play in ensuring that the use of sensitive data aligns with established privacy protections and minimizes potential risks?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the topics discussed concerning potential harms and oversight in the development of the AI Bill of Rights. It is specific and clear in its intent, seeking information on particular aspects (potential harms and oversight) related to a defined subject (AI Bill of Rights). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question is clear and specific, asking for recommended methodologies to evaluate harmful bias in AI training data while ensuring the system's overall safety and reliability. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on evaluation methodologies with a focus on safety and reliability.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does DARPA's XAI play in addressing the challenges posed by opaque AI decision-making in various sectors?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of creating measurement error models for pre-deployment metrics in the context of TEVV processes?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for examples of automated systems mentioned in the technical companion to the AI Bill of Rights. While it specifies the source (technical companion to the AI Bill of Rights) and the type of information sought (examples of automated systems), it assumes access to this specific document without providing its content or context. This reliance on an external reference makes the question unclear for those who do not have access to or are unfamiliar with the document. To improve clarity and answerability, the question could include a brief description or context of the technical companion or frame the question in a way that does not rely on specific, unpublished documents.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are some examples of automated systems mentioned in the technical companion to the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What methods help assess bias in AI training data while ensuring safety?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Safe and effective systems', 'Automated systems', 'Pre-deployment testing', 'Risk identification and mitigation', 'Independent evaluation']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issue highlighted by the digital divide in the context of access to unemployment benefits for individuals. It is clear in its intent, seeking to understand the specific problem related to access caused by the digital divide. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What issue does the digital divide in unemployment benefits highlight in relation to access for individuals?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the ethical guidelines for AI usage in the intelligence sector and their alignment with NIST's standards for safe and transparent AI development. It is clear in specifying the topic of interest (ethical guidelines for AI in the intelligence sector) and the comparison criteria (alignment with NIST's standards). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for the purpose of transparency policies, while the second question focuses on how these policies help manage risks and ensure compliance. These questions have different requirements and depths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of using real-time auditing tools specifically in the context of AI-generated data. It is clear in specifying the topic of interest (real-time auditing tools) and the context (AI-generated data), making the intent straightforward and understandable. The question is self-contained and does not rely on external references or additional context to be answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of using real-time auditing tools in the context of AI-generated data?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to ensure that automated systems are safe and effective?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Digital content transparency', 'Harmful bias', 'Content provenance', 'AI system trustworthiness']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the oversight roles that span from GAI (General Artificial Intelligence) problem formulation to system decommission. It is clear in its intent, seeking information on the various oversight roles involved throughout the lifecycle of a GAI system. The question is specific and does not rely on external references or context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors contributing to model collapse in AI systems, with a specific focus on the reliance on synthetic data and the potential for harmful biases. It is clear in its intent, seeking an explanation of the causes of model collapse and the role of synthetic data and biases. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What are the AI ethics for intel and their alignment with NIST standards?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for actions to prevent harms associated with automated systems. It is clear in its intent, seeking specific preventive measures. The question is independent and does not rely on external references or unspecified contexts. It is broad but understandable and answerable with sufficient domain knowledge.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What actions can be taken to prevent the harms associated with automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the role of an ethical review in the context of using sensitive data. It seeks to understand how ethical reviews ensure alignment with privacy protections and risk minimization. The intent is unambiguous, and the question is self-contained, not relying on external references or prior knowledge beyond general understanding of ethical reviews and privacy protections.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of harmful bias in the context of GAI systems and how can it be addressed?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What causes model collapse in AI, especially with synthetic data and biases?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek methods to assess bias in AI training data, with an emphasis on safety and harmful bias. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions that some meetings focused on providing ideas related to the development of the Blueprint for an AI Bill of Rights, and others provided useful general context on the positive use cases, potential harms, and/or oversight possibilities for these technologies. However, specific topics discussed regarding potential harms and oversight are not detailed in the provided context.', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What oversight roles span from GAI problem formulation to system decommission?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The significance of human-AI configuration in managing GAI risks and ensuring information integrity lies in its role in evaluating content lineage and origin, adapting training programs for digital content transparency, and delineating human proficiency tests from GAI capabilities. It also involves continual monitoring of human-GAI configurations and engaging end-users in prototyping and testing activities to address various scenarios, including crisis situations and ethically sensitive contexts.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What challenges does the reliance on digital platforms for unemployment benefits reveal about equitable access for marginalized individuals?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does an ethical review help protect sensitive data?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of DARPA's XAI (Explainable Artificial Intelligence) in addressing challenges related to opaque AI decision-making across various sectors. It is clear in specifying the topic (DARPA's XAI) and the context (challenges of opaque AI decision-making), and it seeks information on the impact or role of XAI in different sectors. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role do real-time auditing tools play in ensuring the authenticity and tracking of AI-generated content while also facilitating effective monitoring and response to system performance issues?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of creating measurement error models for pre-deployment metrics within the context of TEVV (Test, Evaluation, Verification, and Validation) processes. It is clear in specifying the topic of interest (measurement error models, pre-deployment metrics, TEVV processes) and seeks an explanation of the purpose behind this practice. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of creating measurement error models for pre-deployment metrics in the context of TEVV processes?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Transparency policies help manage GAI risks by establishing processes for documenting the origin and history of training data and generated data for GAI applications. This promotes digital content transparency while balancing the proprietary nature of training approaches, thereby ensuring compliance with data privacy, information integrity, and intellectual property standards.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the principles of AI ethics specifically for the intelligence community, while the second question asks about AI ethics for intelligence and their alignment with NIST standards, introducing an additional requirement.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does DARPA's XAI tackle opaque AI decision-making challenges?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The second question specifies additional factors such as synthetic data and biases, which introduces a narrower and more detailed scope compared to the first question.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What proactive measures can be implemented to ensure automated systems are both safe and effective while preventing potential harms, including those arising from unintended uses or algorithmic discrimination?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Preventable harms', 'Ethics review', 'Sepsis prediction model', 'Algorithmic bias']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Notice and explanation', 'Impact on lives', 'Opaque decision-making', 'Algorithmic risk assessment']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for examples of automated systems mentioned in the technical companion to the AI Bill of Rights. It is clear in specifying the source (technical companion to the AI Bill of Rights) and the type of information sought (examples of automated systems). However, it assumes access to the technical companion document, which is not provided within the question. To improve clarity and answerability, the question could include a brief description or context of the technical companion or specify the type of automated systems of interest (e.g., healthcare, finance).', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Sensitive domains', 'Predictive analytics', 'Student data collection', 'Employee data transfer']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure the safety and effectiveness of automated systems. It is clear in its intent, seeking specific actions or strategies. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided. However, it could be improved by specifying the type of automated systems (e.g., industrial robots, AI software) to narrow down the scope and provide more targeted answers.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the oversight functions or roles throughout the GAI lifecycle, requiring similar depth and breadth of information.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the role of an ethical review in the context of sensitive data, focusing on its purpose and protective measures. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of automated systems on individuals' rights and opportunities?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with the transfer of employee data to third-party job verification services?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some of the potential harms associated with the use of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of harmful bias in the context of GAI (General Artificial Intelligence) systems and how it can be addressed. It is clear in specifying the topic of interest (harmful bias in GAI systems) and seeks detailed information on both the implications and potential solutions. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the implications of harmful bias in the context of GAI systems and how can it be addressed?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the challenges that reliance on digital platforms for unemployment benefits reveals regarding equitable access for marginalized individuals. It is clear in its intent, specifying the focus on challenges and equitable access for a particular group (marginalized individuals). The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Equitable design', 'Automated systems', 'Legal protections', 'Proactive equity assessments']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI risks management', 'Risk response options', 'Model release approaches', 'Information security', 'Harmful bias mitigation']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated system', 'Plain language documentation', 'System functioning', 'Outcome explanations', 'User notification']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question asks for the purpose of XAI as referenced by DARPA, while the second question focuses on how DARPA's XAI addresses opaque AI decision-making challenges. These questions have different requirements and depths of inquiry.", 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Unemployment benefits system', 'Fraud detection system', 'Access to pain medication', 'Automated performance evaluation', 'Human alternatives']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What issues arise for marginalized groups with digital unemployment benefits?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to protect individuals from algorithmic discrimination?"
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when determining model release approaches?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of real-time auditing tools in ensuring the authenticity and tracking of AI-generated content, as well as their role in monitoring and responding to system performance issues. It is clear in specifying the tools of interest (real-time auditing tools) and the aspects it wants to explore (authenticity, tracking, monitoring, and response). The intent is clear and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What should designers and developers provide to ensure transparency about the functioning of an automated system?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for proactive measures to ensure the safety and effectiveness of automated systems while preventing potential harms, including unintended uses and algorithmic discrimination. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking strategies or actions that can be implemented to address the mentioned concerns.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Model collapse in AI can occur when model training over-relies on synthetic data, leading to data points disappearing from the distribution of the new model's outputs. This threatens the robustness of the model overall and can result in homogenized outputs, amplifying any homogenization from the model used to generate the synthetic training data.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential issues associated with automated performance evaluation in the workplace?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the biases that need to be documented when creating measurement error models for pre-deployment metrics. It is clear in its intent, seeking specific information about biases in the context of measurement error models and pre-deployment metrics. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To ensure that automated systems are safe and effective, measures should include consultation with diverse communities, stakeholders, and domain experts to identify concerns and risks. Systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring. These measures should demonstrate safety and effectiveness based on intended use, mitigate unsafe outcomes, and adhere to domain-specific standards. Additionally, independent evaluation and reporting should confirm safety and effectiveness, with results made public whenever possible.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What strategies can be employed to mitigate harmful bias in GAI systems while ensuring the accuracy and integrity of the data used throughout their lifecycle?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do real-time auditing tools help with AI content authenticity and system monitoring?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the potential risks associated with transferring employee data to third-party job verification services. It does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. The intent is clear, seeking information on the risks involved in this data transfer process.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the potential risks associated with the transfer of employee data to third-party job verification services?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps can ensure automated systems are safe and effective, avoiding harms like misuse or bias?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Fairness and bias', 'Systemic bias assessment', 'GAI system outputs', 'Harmful bias and homogenization', 'Training data bias']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What biases must be documented when creating measurement error models for pre-deployment metrics?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about potential harms associated with the use of automated systems. It is clear in its intent, seeking information on the negative impacts or risks of automated systems. The question is independent and does not rely on external references or additional context to be understood. It is specific enough to be answerable by someone with domain knowledge in automated systems or related fields.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are some of the potential harms associated with the use of automated systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address issues related to digital unemployment benefits and access, particularly for marginalized groups. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the implications of automated systems on individuals' rights and opportunities. It is clear in its intent, seeking information on the effects of automation on specific aspects of human life (rights and opportunities). The question is broad but understandable and does not rely on external references or context. However, it could be improved by specifying the type of automated systems (e.g., AI, robotics) or the context (e.g., workplace, legal systems) to narrow down the scope and provide a more focused answer.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the implications of automated systems on individuals' rights and opportunities?"
[ragas.testset.evolutions.INFO] seed question generated: "What methods are suggested for conducting a systemic bias assessment in GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Automated systems', 'Timely human consideration', 'Fallback and escalation process', 'Sensitive domains']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated sentiment analyzer', 'Bias against Jews and gay people', 'Search engine results for minority groups', 'Advertisement delivery systems and stereotypes', 'Algorithmic discrimination in healthcare']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for determining model release approaches. It is clear in its intent, seeking information on factors to consider when deciding how to release a model. The question is independent and does not rely on external references or unspecified contexts. It is specific enough to be understood and answered by someone with domain knowledge in model release strategies.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What considerations should be taken into account when determining model release approaches?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek measures to prevent harms associated with automated systems, including misuse or bias, and require similar depth and breadth in the response.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what designers and developers should provide to ensure transparency about the functioning of an automated system. It is clear in its intent, seeking specific information on the measures or elements necessary for transparency in automated systems. The question is independent and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What should designers and developers provide to ensure transparency about the functioning of an automated system?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the purpose of using real-time auditing tools specifically for AI-generated data, while the second question asks about how these tools help with AI content authenticity and system monitoring. The scope and requirements differ.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to protect individuals from algorithmic discrimination. It is clear in its intent, seeking specific actions or strategies to address a well-defined issue (algorithmic discrimination). The question is independent and does not rely on external references or prior knowledge beyond a general understanding of algorithmic discrimination. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What measures should be taken to protect individuals from algorithmic discrimination?"
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.evolutions.INFO] seed question generated: "What issues does the automated sentiment analyzer address regarding bias in online statements?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Information integrity', 'Human-AI configuration', 'Digital content transparency', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about potential issues associated with automated performance evaluation in the workplace. It is clear in its intent, seeking information on the drawbacks or challenges of using automated systems for performance evaluation. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the potential issues associated with automated performance evaluation in the workplace?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for the purpose of creating measurement error models in the context of TEVV processes, which is a broader inquiry. The second question specifically asks about biases to note in these models, which is a narrower and more specific aspect.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What are the risks and unintended consequences associated with automated systems that may arise from their design, data reliance, and deployment practices?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of digital content transparency in the context of GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for strategies to mitigate harmful bias in GAI (General Artificial Intelligence) systems while ensuring the accuracy and integrity of the data used throughout their lifecycle. It is clear in specifying the topic of interest (mitigating bias in GAI systems) and the dual focus on both bias mitigation and data integrity. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What effects do automated systems have on individual rights and opportunities, and how do current laws and practices address the need for transparency in their decision-making processes?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['TEVV metrics', 'Measurement error models', 'GAI system risks', 'Feedback processes', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for methods suggested for conducting a systemic bias assessment in GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (systemic bias assessment in GAI systems) and seeks detailed information on the methods used. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What methods are suggested for conducting a systemic bias assessment in GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the risks associated with third-party job verification using employee data, specifically considering potential misuse. It is clear in its intent, seeking information on the risks involved. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What factors should be evaluated when selecting model release strategies, considering both risk management approaches and feedback mechanisms from organizational oversight?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What challenges arise from the reliance on automated performance evaluations in workplaces, particularly regarding the absence of human oversight and the potential for biased outcomes?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the suggested actions to evaluate potential biases and stereotypes related to harmful bias and homogenization in AI-generated content?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions documenting biases or statistical variance in applied metrics or structured human feedback processes, particularly when modeling complex societal constructs such as hateful content. However, it does not specify particular biases to note for pre-deployment measurement error models.', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What risks arise from third-party job verification using employee data, considering potential misuse?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Real-time auditing tools aid in the tracking and validation of the lineage and authenticity of AI-generated data, which is essential for ensuring the integrity and reliability of the content produced by AI systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for proactive steps to ensure equitable design and mitigate algorithmic discrimination effects. It is clear in its intent, seeking specific actions or strategies to address these issues. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key elements that should be included in documentation to clarify the impact of an automated system. It is specific in its intent, seeking information on documentation elements, and does not rely on external references or context. The question is clear and self-contained, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for using automated systems in sensitive domains. It is clear in its intent, seeking information on factors to consider, and does not rely on external references or unspecified contexts. The question is specific and independent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies can reduce bias in GAI while maintaining data accuracy?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the risks and unintended consequences associated with automated systems, specifically focusing on aspects of their design, data reliance, and deployment practices. It is clear in its intent to understand potential negative outcomes related to these systems. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided. The specificity regarding design, data reliance, and deployment practices helps in narrowing down the scope of the answer.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What proactive steps ensure equitable design and mitigate algorithmic discrimination effects?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issues that an automated sentiment analyzer addresses concerning bias in online statements. It is clear in specifying the topic of interest (automated sentiment analyzer) and the specific aspect (bias in online statements). The intent is clear, seeking information on the problems related to bias that the sentiment analyzer aims to solve. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What issues does the automated sentiment analyzer address regarding bias in online statements?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key elements must be included in docs for clarity on an automated system's impact?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of digital content transparency in the context of GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (digital content transparency) and the context (GAI systems), making the intent clear and understandable. The question does not rely on external references or unspecified contexts, making it self-contained and independent. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the significance of digital content transparency in the context of GAI systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What combined strategies are recommended for assessing systemic bias in GAI systems while ensuring equitable outputs across diverse demographic groups?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What risks come with automated systems due to design and data use?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Technical protections', 'Rights of the American public', 'Implementation of principles']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the risks associated with transferring employee data to third-party job verification services, sharing the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the effects of automated systems on individual rights and opportunities and how current laws and practices address the need for transparency in their decision-making processes. It is clear in specifying the topic of interest (automated systems, individual rights, opportunities, transparency) and seeks detailed information on both the effects and the legal/practical responses. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'AI Bill of Rights', 'Civil rights and liberties', 'Equal opportunities', 'Access to critical resources']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 2, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the challenges associated with relying on automated performance evaluations in workplaces. It highlights two particular concerns: the absence of human oversight and the potential for biased outcomes. The intent is clear, and the question is self-contained, not requiring additional context or external references to be understood and answered. It effectively conveys its purpose and allows for a direct and relevant response.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for factors to be evaluated when selecting model release strategies, with a focus on risk management approaches and feedback mechanisms from organizational oversight. It is clear in specifying the topic of interest (model release strategies) and the aspects to consider (risk management and feedback mechanisms). The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What impact do automated systems have on rights, and how are transparency needs met by current laws?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures are proposed in the Blueprint for an AI Bill of Rights to protect the rights of the American public?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to evaluate potential biases and stereotypes related to harmful bias and homogenization in AI-generated content. It is clear in specifying the topic of interest (evaluation of biases and stereotypes in AI-generated content) and seeks actionable information. The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the suggested actions to evaluate potential biases and stereotypes related to harmful bias and homogenization in AI-generated content?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of automated systems that should be covered by the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'When using automated systems in sensitive domains, considerations should include tailoring the systems to their intended purpose, providing meaningful access for oversight, ensuring training for individuals interacting with the system, and incorporating human consideration for adverse or high-risk decisions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the issue of algorithmic discrimination and bias, focusing on measures and steps to mitigate these problems. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What issues come from using automated performance reviews without human oversight?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the implications of harmful bias in GAI systems and how it can be addressed, which involves understanding the consequences and potential solutions. The second question focuses specifically on strategies to reduce bias while maintaining data accuracy, which is narrower in scope. Therefore, they do not share the same depth and breadth of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the potential negative impacts of automated systems, focusing on risks and harms. They share similar constraints and requirements, as well as depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 3 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What biases does the automated sentiment analyzer reveal in online expressions, and how do these biases compare to issues found in predictive policing systems regarding transparency and fairness?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Confabulation', 'Generative AI systems', 'False content', 'Statistical prediction', 'Risks of confabulated content']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Preventable harms', 'Ethics review', 'Sepsis prediction model', 'Algorithmic bias']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question focuses on what designers and developers should provide to ensure transparency about the functioning of an automated system, while the second question asks about key elements that ensure clarity in documentation about an automated system's impact. The focus and requirements differ, leading to different depths and breadths of inquiry.", 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What roles do training and feedback mechanisms play in enhancing the understanding of digital content transparency within GAI systems, particularly regarding societal impacts and content provenance?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What factors to consider for model release strategies regarding risk and oversight?"
[ragas.testset.evolutions.INFO] seed question generated: "What is confabulation in the context of generative AI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of harms caused by algorithmic bias in automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Executive Order 13960', 'Trustworthy Artificial Intelligence', 'AI Bill of Rights', 'NIST AI Risk Management Framework', 'Stakeholder engagement']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Sensitive domains', 'Human oversight', 'Algorithmic discrimination', 'Meaningful access']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for combined strategies to assess systemic bias in GAI (General Artificial Intelligence) systems and ensure equitable outputs across diverse demographic groups. It is clear in its intent, specifying the need for strategies that address both assessment of bias and equity in outputs. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the potential issues of automated performance evaluation in the workplace, specifically focusing on the lack of human oversight. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 3 times
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What methodologies are recommended for assessing biases and stereotypes in AI-generated content while ensuring effective feedback mechanisms from diverse user communities?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question focuses on the implications of automated systems on individuals' rights and opportunities, while the second question addresses the impact on rights and the transparency needs met by current laws. The second question introduces an additional dimension of legal transparency, leading to different depths and breadths of inquiry.", 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies help assess bias in GAI for fair outputs across demographics?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Bill of Rights in relation to the Executive Order on trustworthy artificial intelligence?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to avoid algorithmic discrimination in automated systems used within sensitive domains?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the measures proposed in the 'Blueprint for an AI Bill of Rights' to protect the rights of the American public. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information about the proposed measures in the specified document.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for examples of automated systems that should be included in the Blueprint for an AI Bill of Rights. It is clear in its intent, seeking specific examples of automated systems, and does not rely on external references or prior knowledge beyond understanding what automated systems and the AI Bill of Rights are. The question is self-contained and understandable.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are some examples of automated systems that should be covered by the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions focus on the considerations for model release strategies, including aspects like risk and oversight, sharing the same depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Key elements that ensure clarity in documentation about an automated system's impact include providing generally accessible plain language documentation, clear descriptions of the overall system functioning and the role of automation, timely updates about significant use case or key functionality changes, and explanations of outcomes that are clear, timely, and accessible.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Risk assessment', 'Explanatory mechanisms', 'Transparency in decision-making', 'Summary reporting']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI risks', 'Human-AI interactions', 'Disinformation impact', 'Risk management resources', 'Trustworthy AI Characteristics']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Policies and procedures for human-AI configurations', 'Oversight of GAI systems', 'Risk measurement processes', 'Human-AI configuration', 'Threat modeling for GAI systems']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for examples of harms caused by algorithmic bias in automated systems. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge and clearly seeks information on the negative impacts of algorithmic bias.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for an explanation of 'confabulation' specifically within the context of generative AI systems. It is clear in specifying the term of interest (confabulation) and the context (generative AI systems), making the intent straightforward and understandable. The question is self-contained and does not rely on external references or additional context to be answered. Therefore, it meets the criteria for clarity and answerability.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is confabulation in the context of generative AI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the roles of training and feedback mechanisms in enhancing the understanding of digital content transparency within GAI (General Artificial Intelligence) systems, with a focus on societal impacts and content provenance. It is specific in its scope (training and feedback mechanisms, digital content transparency, societal impacts, content provenance) and clear in its intent, seeking an explanation of how these mechanisms contribute to the understanding of transparency. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about biases revealed by an automated sentiment analyzer in online expressions and seeks a comparison of these biases with issues found in predictive policing systems, specifically regarding transparency and fairness. The intent is clear, as it specifies the areas of interest (biases, transparency, fairness) and the systems to be compared (sentiment analyzer, predictive policing). However, it assumes familiarity with the specific biases and issues in both systems without providing context or examples. To improve clarity and answerability, the question could benefit from briefly describing the types of biases or issues typically associated with these systems or specifying particular aspects of transparency and fairness to be compared.', 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What biases does the automated sentiment analyzer reveal in online expressions, and how do these biases compare to issues found in predictive policing systems regarding transparency and fairness?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek methods or strategies for assessing bias in General Artificial Intelligence (GAI) systems to ensure fairness across demographics. They share the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What role does transparency in decision-making play in the design of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Information sharing and feedback mechanisms', 'AI impact assessment', 'Organizational policies', 'Third-party rights']
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with human-AI interactions?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of engaging in threat modeling for GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear in its intent, asking for recommended methodologies to assess biases and stereotypes in AI-generated content and how to ensure effective feedback mechanisms from diverse user communities. It is specific and does not rely on external references or prior knowledge not included in the question itself. The question is self-contained and understandable, making it answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do training and feedback improve understanding of digital content transparency in GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI trustworthy characteristics', 'Human-AI Configuration', 'Information Integrity', 'Data Privacy', 'Confabulation']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What types of automated systems, impacting civil rights, equal opportunities, and access to essential services, should be included in the AI Bill of Rights framework?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The Blueprint for an AI Bill of Rights proposes a set of five principles and associated practices to guide the design, use, and deployment of automated systems to protect the rights of the American public. It includes expectations for automated systems, practical steps for implementation, and emphasizes transparency through reporting to ensure that rights, opportunities, and access are respected.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role do organizational policies play in addressing AI risks associated with third-party entities?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the purpose of the AI Bill of Rights in relation to the Executive Order on trustworthy artificial intelligence. It is clear in specifying the two documents of interest (AI Bill of Rights and Executive Order on trustworthy AI) and seeks information on their relationship. The intent is clear, and the question is self-contained, not relying on external references or additional context. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated systems significantly impact rights by determining opportunities in various areas such as employment and credit, often without individuals being aware of the algorithms influencing decisions. Current laws, such as the Biometric Information Privacy Act in Illinois, require written notice when biometric information is used, and federal laws like the Fair Credit Reporting Act mandate that consumers receive adverse action notices when credit is denied. These laws aim to ensure transparency and provide individuals with the knowledge necessary to contest decisions made by automated systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to avoid algorithmic discrimination in automated systems used within sensitive domains. It is clear in its intent, seeking specific actions or strategies to address a well-defined issue (algorithmic discrimination) within a specified context (sensitive domains). The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of algorithmic discrimination and automated systems. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What measures should be taken to avoid algorithmic discrimination in automated systems used within sensitive domains?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What methods work for evaluating biases in AI content with diverse user feedback?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'White House Office of Science and Technology Policy', 'Automated systems', 'Civil rights and democratic values', 'National security and defense activities']
[ragas.testset.evolutions.INFO] seed question generated: "What is the role of the White House Office of Science and Technology Policy in relation to the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of transparency in decision-making within the context of designing automated systems. It is clear in specifying the topic of interest (transparency in decision-making) and the context (design of automated systems). The intent is to understand the impact or importance of transparency in this specific area, making it understandable and answerable without needing additional context or external references.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does transparency in decision-making play in the design of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential risks associated with human-AI interactions. It is clear in its intent, seeking information on the risks involved in interactions between humans and AI. The question is independent and does not rely on external references or additional context to be understood. It is specific enough to be answerable by someone with domain knowledge in AI and human-computer interaction.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the potential risks associated with human-AI interactions?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about biases revealed by an automated sentiment analyzer in online expressions and seeks a comparison of these biases with issues found in predictive policing systems, specifically regarding transparency and fairness. The intent is clear, aiming to explore both the nature of biases in sentiment analysis and their parallels with predictive policing. However, the question assumes familiarity with the specific biases in both domains without providing context or examples. To improve clarity and answerability, the question could benefit from specifying the types of biases or providing a brief description of the known issues in both sentiment analysis and predictive policing systems.', 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account regarding data privacy when deploying a GAI system?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of engaging in threat modeling for GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (threat modeling for GAI systems) and seeks an explanation of the purpose behind this activity. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of engaging in threat modeling for GAI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on evaluating potential biases and stereotypes specifically related to harmful bias and homogenization in AI-generated content, while the second question is broader, asking about methods for evaluating biases in AI content with diverse user feedback. The depth and breadth of the inquiries differ.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for types of automated systems that impact civil rights, equal opportunities, and access to essential services, which should be included in the AI Bill of Rights framework. It is clear in its intent, specifying the areas of impact and the context of the AI Bill of Rights framework. The question is self-contained and does not rely on external references or prior knowledge not provided within the question itself. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What actions are suggested to address risks associated with intellectual property infringement in organizational GAI systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Examples of harms caused by algorithmic bias in automated systems include: 1) A proprietary model predicting sepsis in hospitalized patients that underperformed and caused alert fatigue by falsely alerting likelihood of sepsis. 2) An automated moderation system on social media that silenced Black people who quoted and criticized racist messages, failing to distinguish their counter speech from the original hateful messages. 3) A device meant to help track lost items being misused by stalkers to track victims' locations, despite manufacturer attempts to implement safety measures. 4) An algorithm used for police deployment that sent officers to neighborhoods they regularly visited, rather than those with the highest crime rates, due to a feedback loop from previous data and predictions.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of organizational policies in addressing AI risks associated with third-party entities. It is clear in specifying the topic of interest (organizational policies) and the context (AI risks with third-party entities). The intent is straightforward, seeking an explanation of how these policies mitigate or manage such risks. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role do organizational policies play in addressing AI risks associated with third-party entities?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for a specific term that describes the generation of false content by GAI (General Artificial Intelligence) that misleads users into trusting it. The intent is clear, seeking a specific term related to a well-defined concept. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The Blueprint for an AI Bill of Rights is consistent with the Executive Order 13960 on Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government, which requires federal agencies to adhere to nine principles when using AI.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Fairness in Artificial Intelligence', 'Automatic signature verification', 'Ballot curing', 'Digital divide in unemployment benefits', 'Racial equity and underserved communities']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the significance of digital content transparency in GAI systems, while the second question is about how training and feedback improve understanding of this transparency. They have different constraints and requirements, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What automated systems affecting civil rights and access to services belong in the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Automated systems', 'Bias testing', 'Equitable design', 'Systemic biases']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What term describes GAI's generation of false content that misleads users into trusting it?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What significance does the clarity and accessibility of decision-making explanations hold in the context of automated systems, particularly regarding risk assessment and user understanding?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key human oversight measures that prevent algorithmic discrimination in sensitive automated systems. It is clear in specifying the topic of interest (human oversight measures) and the context (preventing algorithmic discrimination in sensitive automated systems). The intent is clear, and the question is independent, as it does not rely on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of the White House Office of Science and Technology Policy (OSTP) in relation to the AI Bill of Rights. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the role of the White House Office of Science and Technology Policy in relation to the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What multifaceted dangers arise from human interactions with generative AI, considering both immediate emotional impacts and broader societal implications?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of the NSF Program on Fairness in Artificial Intelligence in collaboration with Amazon?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does threat modeling play in identifying and mitigating risks associated with GAI systems, particularly in relation to organizational policies on transparency and risk management?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Artificial Intelligence and Democratic Values', 'Non-discriminatory technology', 'Explainable AI', 'Community participation', 'Social welfare systems']
[ragas.testset.evolutions.INFO] seed question generated: "What role does bias testing play in preventing algorithmic discrimination in automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to address risks associated with intellectual property infringement in organizational GAI (General Artificial Intelligence) systems. It is specific in its focus on intellectual property infringement and organizational GAI systems, and it clearly seeks actionable recommendations. The intent is clear, and the question is self-contained, not relying on external references or additional context. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key human oversight measures prevent algorithmic discrimination in sensitive automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for considerations regarding data privacy when deploying a GAI (General Artificial Intelligence) system. It is clear in specifying the topic of interest (data privacy) and the context (deploying a GAI system). The intent is straightforward, seeking information on the factors to consider for data privacy in this specific scenario. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What considerations should be taken into account regarding data privacy when deploying a GAI system?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about automated systems relevant to the AI Bill of Rights, focusing on civil rights and access to services, thus sharing the same depth, breadth, and requirements.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What role does technology play in implementing or improving social welfare systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What significance do organizational policies hold in mitigating AI-related risks posed by third-party entities, particularly in the context of oversight and impact documentation throughout the AI lifecycle?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions evaluating potential biases and stereotypes that could emerge from AI-generated content using appropriate methodologies, including computational testing methods as well as evaluating structured feedback input. Additionally, it suggests recording and integrating structured feedback about content provenance from operators, users, and potentially impacted communities through methods such as user research studies, focus groups, or community forums.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to demonstrate the safety and effectiveness of automated systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question asks for an explanation of 'confabulation' specifically in the context of generative AI systems, while the second question is looking for a term that refers to misleading false content generated by GAI. The depth and breadth of the inquiries differ as the first is more specific and the second is more general.", 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What functions does the OSTP serve in shaping the AI Bill of Rights, particularly in relation to public input and the protection of civil liberties?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Training and feedback improve understanding of digital content transparency in GAI systems by providing input for training materials about the capabilities and limitations of GAI systems related to digital content transparency. This includes actively seeking feedback on generated content quality and potential biases, as well as assessing the general awareness among end users and impacted communities about the availability of feedback channels.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the issue of preventing algorithmic discrimination or bias in sensitive systems, requiring similar measures and safeguards. They share the same depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of clarity and accessibility of decision-making explanations in automated systems, specifically in the context of risk assessment and user understanding. It is clear in its intent, seeking to understand the importance of these factors. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear in its intent, asking about the dangers of human interactions with generative AI, specifically focusing on both immediate emotional impacts and broader societal implications. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The question is specific enough to allow for a detailed and relevant response, covering both emotional and societal dimensions.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of the NSF Program on Fairness in Artificial Intelligence in collaboration with Amazon. It is clear in specifying the program of interest and seeks information on its importance or impact. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The suggested action to address risks associated with intellectual property infringement in organizational GAI systems is to compile statistics on actual policy violations, take-down requests, and intellectual property infringement, and analyze transparency reports across demographic and language groups.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Civil rights and liberties', 'Public consultation', 'Algorithmic harms']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What multifaceted factors regarding data privacy and content integrity must be evaluated when implementing a GAI system, particularly in relation to user feedback and the system's operational transparency?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How important are clear decision-making explanations in automated systems for risk assessment and user understanding?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of threat modeling in identifying and mitigating risks associated with GAI (General Artificial Intelligence) systems, specifically in the context of organizational policies on transparency and risk management. It is clear in specifying the topic of interest (threat modeling, GAI systems) and the specific aspects of organizational policies it relates to (transparency, risk management). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does the 2023 Executive Order on Safe AI play in NIST's efforts to develop trustworthy artificial intelligence?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What risks come from human use of generative AI, both emotionally and socially?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of technology in implementing or improving social welfare systems. It is clear in its intent, seeking information on the impact or contribution of technology in this specific context. The question is independent and does not rely on external references or prior knowledge not included within the question itself. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The term that refers to GAI\'s misleading false content is "confabulation."', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.INFO] seed question generated: "What role did public input play in addressing algorithmic harms in the Blueprint for an AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does threat modeling help with GAI risk and org policies on transparency?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI risks management', 'Risk response options', 'Model release approaches', 'Information security', 'Harmful bias mitigation']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of organizational policies in mitigating AI-related risks posed by third-party entities, with a focus on oversight and impact documentation throughout the AI lifecycle. It is specific in its scope (organizational policies, AI-related risks, third-party entities) and clear in its intent (understanding the role of policies in risk mitigation). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of bias testing in preventing algorithmic discrimination in automated systems. It is clear in specifying the topic of interest (bias testing) and the context (preventing algorithmic discrimination in automated systems). The intent is straightforward, seeking an explanation of the importance and impact of bias testing. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does bias testing play in preventing algorithmic discrimination in automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to demonstrate the safety and effectiveness of automated systems. It is clear in its intent, seeking specific actions or protocols that can be implemented to ensure these systems are safe and effective. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What measures should be taken to demonstrate the safety and effectiveness of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data protection', 'Privacy by design', 'User consent', 'Sensitive domains', 'Surveillance technologies']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address the importance of decision-making transparency in automated systems, the second question specifically focuses on risk assessment and user understanding, which adds additional constraints and depth.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What techniques can be employed to mitigate harmful bias in AI-generated content?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do org policies help manage AI risks from third parties during the AI lifecycle?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the functions of the OSTP (Office of Science and Technology Policy) in shaping the AI Bill of Rights, with a focus on public input and the protection of civil liberties. It is clear in specifying the topic (OSTP's role in the AI Bill of Rights) and the aspects of interest (public input and civil liberties). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Supplier risk assessment framework', 'Third-party entities', 'Content provenance standards', 'GAI technology and service provider lists', 'Intellectual property and data privacy']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question broadly asks about potential risks associated with human-AI interactions, while the second question specifically focuses on emotional and social risks from the use of generative AI. This difference in scope and specificity leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What role does the OSTP play in the AI Bill of Rights regarding public input and civil liberties?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the general purpose of engaging in threat modeling for GAI systems, while the second question specifically asks about how threat modeling helps with GAI risk and organizational policies on transparency. These questions have different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of content provenance standards in evaluating third-party entities' performance?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does user consent play in the collection and use of personal data?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Generative AI Public Working Group', 'GAI risk management', 'Governance', 'Content Provenance', 'AI lifecycle risks']
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to demonstrate the safety and effectiveness of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of the 2023 Executive Order on Safe AI in NIST's efforts to develop trustworthy artificial intelligence. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information on the impact of a specific executive order on NIST's AI development efforts.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does the 2023 Executive Order on Safe AI play in NIST's efforts to develop trustworthy artificial intelligence?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the multifaceted factors related to data privacy and content integrity that need to be evaluated when implementing a GAI (Generative AI) system, with a particular focus on user feedback and the system's operational transparency. It is clear in specifying the areas of interest (data privacy, content integrity, user feedback, operational transparency) and seeks detailed information on these aspects. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What proactive measures, including bias testing, are essential for ensuring that automated systems are designed and deployed equitably to prevent algorithmic discrimination across various demographics?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the role of organizational policies in managing AI risks associated with third-party entities, covering similar constraints and requirements, and having the same depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of public input in addressing algorithmic harms within the context of the Blueprint for an AI Bill of Rights. It is clear in specifying the topic of interest (public input, algorithmic harms, Blueprint for an AI Bill of Rights) and seeks detailed information on the impact of public input. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role did public input play in addressing algorithmic harms in the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the steps that ensure independent evaluation and reporting for automated system safety. It is clear in its intent, seeking specific steps or procedures related to the evaluation and reporting of safety in automated systems. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What factors on data privacy and content integrity should be considered for a GAI system, especially regarding user feedback and transparency?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated system', 'Plain language documentation', 'System functioning', 'Outcome explanations', 'User notification']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context emphasizes the importance of clear decision-making explanations in automated systems, particularly for risk assessment and user understanding. It states that explanations should accurately reflect the factors influencing decisions and be tailored to the purpose and audience, ensuring they are understandable and accessible. This is crucial in settings with high consequences, where transparency and clarity are necessary for accountability and user trust.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What steps ensure independent evaluation and reporting for automated system safety?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the primary considerations addressed in GAI risk management as outlined by NIST's Generative AI Public Working Group?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "While both questions inquire about the OSTP's role in the AI Bill of Rights, the second question specifically focuses on public input and civil liberties, adding additional constraints and depth.", 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for techniques to mitigate harmful bias in AI-generated content. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information on methods to address bias in AI content generation.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What techniques can be employed to mitigate harmful bias in AI-generated content?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The risks that come from human use of generative AI (GAI) include immediate and prolonged emotional harm, potential risks to physical safety due to the distribution of harmful deepfake images, and the long-term effect of disinformation on societal trust in public institutions.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What should users be notified about regarding automated systems that impact them?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Algorithmic discrimination', 'Automated systems', 'Community impacts', 'Equity and access']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What significance does the 2023 Executive Order on Safe AI hold in shaping NIST's framework for ensuring the reliability and security of artificial intelligence systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of user consent in the collection and use of personal data. It is clear in its intent, seeking information on the importance and implications of user consent in this context. The question is independent and does not rely on external references or additional context to be understood. It is specific and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to demonstrate the safety and effectiveness of automated systems. It is clear in its intent, seeking specific actions or protocols that can be implemented to ensure these systems are safe and effective. The question is independent and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the significance of content provenance standards in evaluating third-party entities' performance. It is clear in specifying the topic of interest (content provenance standards) and the context (evaluating third-party entities' performance). The intent is also clear, seeking an explanation of the importance or impact of these standards. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the significance of content provenance standards in evaluating third-party entities' performance?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The second question introduces additional factors such as content integrity, user feedback, and transparency, which are not explicitly mentioned in the first question. This leads to a broader and deeper inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Public consultation', 'Testing and deployment', 'Risk identification and mitigation', 'Safety and effectiveness']
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of evaluating the harms of automated systems at the community level according to the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for measures to demonstrate safety and effectiveness of automated systems, while the second question focuses on independent evaluation and reporting for system safety. These questions have different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Safe and effective systems', 'Automated systems', 'Pre-deployment testing', 'Risk identification and mitigation', 'Independent evaluation']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear in its intent, asking for proactive measures, including bias testing, that are essential for ensuring equitable design and deployment of automated systems to prevent algorithmic discrimination across various demographics. It is specific and does not rely on external references or context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Safe and effective systems', 'Automated systems', 'Pre-deployment testing', 'Risk identification and mitigation', 'Independent evaluation']
[ragas.testset.evolutions.INFO] seed question generated: "What are the expectations for ensuring that automated systems are safe and effective?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What steps, like bias testing, ensure fair automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connections between public input and the shaping of the AI Bill of Rights' principles. It is clear in its intent, seeking to understand the influence of public input on the development of these principles. The question is specific and does not rely on external references or prior knowledge beyond a general understanding of the AI Bill of Rights. Therefore, it meets the criteria for independence and clear intent.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of pre-deployment testing in the development of automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of pre-deployment testing in the development of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what users should be notified about regarding automated systems that impact them. It is clear in its intent, seeking information on the types of notifications or disclosures that should be provided to users. The question is independent and does not rely on external references or unspecified contexts. It is specific enough to be understood and answered based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What should users be notified about regarding automated systems that impact them?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The Office of Science and Technology Policy (OSTP) plays a crucial role in the AI Bill of Rights by leading a year-long process to seek and distill input from various stakeholders, including impacted communities, industry stakeholders, technology developers, and policymakers. This engagement informs the development of policies and practices that protect civil rights and promote democratic values in the governance of automated systems.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between public input and shaping the AI Bill of Rights' principles?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'User consent plays a crucial role in the collection and use of personal data, as it should only be used to justify data collection in cases where it can be appropriately and meaningfully given. Consent requests should be brief, understandable in plain language, and provide individuals with agency over data collection and its specific context of use.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for methods to reduce harmful bias in AI outputs while ensuring content integrity. It is clear in its intent, specifying the dual goals of bias reduction and content integrity. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role do content provenance standards play in assessing the performance and risks associated with third-party GAI systems, particularly in relation to information integrity and intellectual property?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for the primary considerations in GAI risk management as outlined by NIST's Generative AI Public Working Group. It is clear in specifying the topic (GAI risk management) and the source (NIST's Generative AI Public Working Group), making the intent clear. However, it assumes familiarity with the specific document or findings of the NIST's Generative AI Public Working Group without providing any context or summary of their work. To improve clarity and answerability, the question could include a brief description or key points from the NIST's Generative AI Public Working Group's findings, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are the primary considerations addressed in GAI risk management as outlined by NIST's Generative AI Public Working Group?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What methods can reduce harmful bias in AI outputs while ensuring content integrity?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the significance of the 2023 Executive Order on Safe AI in shaping NIST's framework for ensuring the reliability and security of artificial intelligence systems. It is clear in specifying the topic of interest (2023 Executive Order on Safe AI) and the specific aspect of NIST's framework it impacts (reliability and security of AI systems). The intent is clear, seeking an explanation of the influence or role of the Executive Order. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To demonstrate the safety and effectiveness of automated systems, the following measures should be taken: 1. Independent evaluation should be allowed, enabling access for independent evaluators such as researchers and auditors to the system and associated data. 2. Reporting should be regularly updated, including an overview of the system, data used for training, risk assessments, performance testing results, and ongoing monitoring procedures. Reports should be provided in plain language and machine-readable formats.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated system', 'Plain language documentation', 'System functioning', 'Outcome explanations', 'User notification']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of evaluating the harms of automated systems at the community level according to the Blueprint for an AI Bill of Rights. It is clear in specifying the topic of interest (harms of automated systems, community level, Blueprint for an AI Bill of Rights) and seeks detailed information on the significance of this evaluation. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the significance of evaluating the harms of automated systems at the community level according to the Blueprint for an AI Bill of Rights?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address the prevention of algorithmic discrimination in automated systems, the first question specifically focuses on the role of bias testing, whereas the second question is broader and asks about various steps, including but not limited to bias testing. This difference in specificity leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Factors on data privacy and content integrity for a GAI system include documenting the extent to which human domain knowledge is employed to improve GAI system performance, reviewing and verifying sources and citations in GAI system outputs, tracking instances of anthropomorphization in GAI system interfaces, verifying GAI system training data and TEVV data provenance, and regularly reviewing security and safety guardrails. Additionally, structured feedback about content provenance should be recorded and integrated from operators, users, and impacted communities, and there should be an emphasis on digital content transparency regarding the societal impacts of AI and the role of diverse and inclusive content generation.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of pre-deployment testing in the development of automated systems. It is clear and specific, seeking information about the role and importance of this testing phase. The question is self-contained and does not rely on external references or additional context to be understood or answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the expectations for ensuring that automated systems are safe and effective. It is clear in its intent, seeking information on safety and effectiveness standards or guidelines for automated systems. The question is independent and does not rely on external references or unspecified contexts. However, it could be improved by specifying the type of automated systems (e.g., industrial robots, autonomous vehicles) or the context (e.g., regulatory, operational) to narrow down the scope and provide a more targeted answer.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Independent evaluation for system safety is ensured by designing automated systems to allow for independent evaluation through mechanisms such as application programming interfaces. Independent evaluators, including researchers, journalists, ethics review boards, inspectors general, and third-party auditors, should have access to the system and samples of associated data, consistent with privacy, security, law, or regulation. Additionally, entities responsible for automated systems should provide regularly-updated reports that include an overview of the system, data used, risk assessments, performance testing results, and independent evaluation outcomes, all presented in plain language and a machine-readable format.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of pre-deployment testing in the development of automated systems. It is clear and specific, seeking information about the role and importance of this testing phase. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of pre-deployment testing in the development of automated systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about the role of public input in addressing algorithmic harms within the Blueprint for an AI Bill of Rights, while the second question is broader, asking generally about the influence of public input on the AI Bill of Rights. The depth and breadth of the inquiries differ.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What should designers and developers provide to ensure clear understanding of system functioning in automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What role does human-AI integration play in enhancing customer service?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 1, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Race correction in clinical algorithms', 'Algorithmic impact assessment', 'Racial equity', 'Algorithmic bias detection', 'Property appraisal and valuation equity', 'Executive Order on Advancing Racial Equity', 'Supreme Court Decision Roe v. Wade', 'Bill of Rights for an Automated Society', 'Sepsis prediction model', 'Apple AirTags stalking concerns']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key information that users must receive about automated systems affecting their outcomes. It is clear in its intent, seeking specific details about the necessary information users should be provided with. The question is independent and does not rely on external references or additional context to be understood. It is specific and straightforward, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of content provenance standards in evaluating the performance and risks of third-party GAI (Generative AI) systems, with a focus on information integrity and intellectual property. It is specific and clear in its intent, seeking to understand the impact of these standards on particular aspects of GAI systems. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Identity theft', 'Facial recognition system', 'Surveillance software', 'Employee discussions about union activity']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Transparency artifacts', 'Explainable AI (XAI)', 'Pre-trained models', 'Harmful bias', 'Content filters']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does community-level assessment play in mitigating algorithmic discrimination as outlined in the AI Bill of Rights?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the primary considerations in GAI risk management as outlined by NIST's Generative AI Public Working Group. It is specific in its focus on GAI risk management and the NIST Generative AI Public Working Group, making the intent clear. However, it assumes familiarity with the specific document or findings of the NIST Generative AI Public Working Group without providing any context or summary of these considerations. To improve clarity and answerability, the question could include a brief description or key points from the NIST document, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of algorithmic impact assessment as discussed in the context?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key info must users receive about automated systems affecting their outcomes?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do content provenance standards impact the performance and risks of third-party GAI systems regarding info integrity and IP?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of how data privacy principles aim to protect against identity theft?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does extensive testing play in ensuring the safety and effectiveness of automated systems prior to their deployment, particularly in relation to community consultation and risk mitigation?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of pre-deployment testing in the development of automated systems is to identify risks and potential impacts of the system, ensuring that it is safe and effective based on its intended use, and to mitigate unsafe outcomes, including those beyond the intended use.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of reviewing transparency artifacts in the context of third-party models?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does the 2023 Exec Order on Safe AI impact NIST's AI reliability and security framework?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what designers and developers should provide to ensure a clear understanding of system functioning in automated systems. It is specific and independent, as it does not rely on external references or prior knowledge. The intent is clear, seeking information on best practices or necessary elements for clarity in automated systems. No improvements are necessary.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What should designers and developers provide to ensure clear understanding of system functioning in automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Public input influences the AI Bill of Rights by providing insights and feedback from impacted communities, industry stakeholders, technology developers, and experts. The White House Office of Science and Technology Policy conducted a year-long process to gather this input through various means, including panel discussions and public listening sessions, which helped shape the principles and practices outlined in the Blueprint for an AI Bill of Rights.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of human-AI integration in enhancing customer service. It is clear in its intent, seeking information on the impact or benefits of combining human and AI efforts in the context of customer service. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What role does human-AI integration play in enhancing customer service?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Many companies, non-profits, and federal government agencies are taking steps to ensure the public is protected from algorithmic discrimination. Some companies have instituted bias testing as part of their product quality assessment and launch procedures, which has led to products being changed or not launched to prevent harm. Federal government agencies are developing standards and guidance for the use of automated systems to help prevent bias. Non-profits and companies have developed best practices for audits and impact assessments to identify potential algorithmic discrimination and provide transparency in mitigating such biases.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek methods to reduce bias in AI-generated content, requiring similar constraints and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 3 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Pre-deployment testing', 'GAI system validity', 'Measurement gaps', 'Structured public feedback', 'AI Red-teaming']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions ask for the key information or notifications that users should receive about automated systems impacting them, sharing the same depth, breadth, and requirements for the answer.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The expectations for ensuring that automated systems are safe and effective include: 1) Safeguards to protect the public from harm in a proactive and ongoing manner; 2) Avoiding the use of data that is inappropriate or irrelevant to the task at hand; 3) Demonstrating the safety and effectiveness of the system. Additionally, there should be consultation with the public during the design and implementation phases, extensive testing before deployment, and identification and mitigation of potential risks.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Training data use', 'Intellectual property', 'Data privacy risks', 'Content provenance', 'Generative AI (GAI) risks']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of structured public feedback in evaluating GAI systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question focuses on the significance of content provenance standards in evaluating third-party entities' performance, while the second question addresses the impact of these standards on the performance and risks of third-party GAI systems specifically regarding information integrity and intellectual property. The second question has a broader scope and different depth of inquiry.", 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI lifecycle', 'AI technology risks', 'Organizational practices for AI', 'Impact documentation process', 'Content provenance methodologies']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "Both questions inquire about the influence of the 2023 Executive Order on Safe AI on NIST's initiatives related to AI trustworthiness, reliability, and security, sharing the same depth, breadth, and requirements for the answer.", 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 2 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for examples of how data privacy principles protect against identity theft. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on the application of data privacy principles in the context of identity theft protection.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI technology mapping', 'Legal risks', 'Data privacy', 'Intellectual property', 'Harmful biases']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of community-level assessment in mitigating algorithmic discrimination as outlined in the AI Bill of Rights. It is specific in its focus on community-level assessment and its relation to algorithmic discrimination, and it references the AI Bill of Rights, which provides a clear context. The intent is clear, seeking an explanation of the role and impact of community-level assessment within the specified framework. The question is self-contained and does not rely on external references beyond the AI Bill of Rights, which is sufficiently well-known to provide necessary context.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account regarding intellectual property when conducting diligence on training data use?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What essential elements must be included in the documentation and notifications provided by designers and developers to ensure users comprehend the functioning and decision-making processes of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of reviewing transparency artifacts in the context of third-party models. It is clear in specifying the topic of interest (transparency artifacts, third-party models) and seeks information on the purpose of this review. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of reviewing transparency artifacts in the context of third-party models?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of extensive testing in ensuring the safety and effectiveness of automated systems before deployment, with a particular focus on community consultation and risk mitigation. It is clear in its intent, specifying the aspects of safety, effectiveness, community consultation, and risk mitigation. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the purpose of algorithmic impact assessment but refers to 'the context' without providing or describing this context within the query. This makes the question unclear for those who do not have access to the unspecified context. For the question to be clear and answerable, it needs to either include the relevant context directly within the question or be framed in a way that does not require external information. Detailing the specific aspects or scenarios of algorithmic impact assessment being referred to could also help clarify the query.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What is the purpose of algorithmic impact assessment as discussed in the context?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key oversight functions involved in the GAI lifecycle?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Predictive policing system', 'Gun violence risk assessment', 'Watch list transparency', 'System flaws in benefit allocation', 'Lack of explanation for decisions']
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to address data privacy risks in AI-generated content?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does community assessment help reduce algorithmic bias in the AI Bill of Rights?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does testing ensure the safety of automated systems before deployment, especially regarding community input and risk?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What issues arise from system flaws in benefit allocation?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['CBRN Information', 'Confabulation', 'Dangerous content', 'Data Privacy', 'Harmful Bias']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the benefits of combining AI tools with human agents in customer service. It is clear in its intent, seeking information on the advantages of this combination. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human subject protection', 'Content provenance', 'Data privacy', 'AI system performance', 'Anonymization techniques']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'User consent', 'Automated systems', 'Surveillance technologies', 'Sensitive domains']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What benefits arise from combining AI tools with human agents in customer service?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of eased access to dangerous content in relation to violent or hateful material?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Examples of how data privacy principles aim to protect against identity theft include: a data broker harvesting large amounts of personal data and suffering a breach that exposes individuals to potential identity theft, and an insurer collecting data from a person's social media presence to determine life insurance rates, which could lead to misuse of personal information.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What requirements must evaluations involving human subjects meet to ensure human subject protection?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations related to intellectual property when conducting diligence on training data use. It is clear in its intent, seeking specific information on intellectual property considerations in the context of training data diligence. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role do transparency artifacts play in ensuring the integrity and security of third-party AI models during their deployment and monitoring?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the significance of evaluating harms at the community level according to the Blueprint for an AI Bill of Rights, while the second question is more specific to how community assessment helps reduce algorithmic bias. They have different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to ensure data privacy and protect individuals from abusive data practices?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Fairness in Artificial Intelligence', 'Automatic signature verification', 'Ballot curing', 'Digital divide in unemployment benefits', 'Racial equity and underserved communities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for the essential elements that should be included in documentation and notifications to help users understand the functioning and decision-making processes of automated systems. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking detailed information on documentation and notification practices for user comprehension in the context of automated systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to address data privacy risks in AI-generated content. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or unspecified contexts, and it clearly seeks information on suggested measures for a particular issue.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What measures are suggested to address data privacy risks in AI-generated content?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the general purpose of pre-deployment testing in automated systems, while the second question specifically addresses how testing ensures safety, with an emphasis on community input and risk. These questions have different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of structured public feedback in evaluating GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (structured public feedback) and the context (evaluating GAI systems). The intent is straightforward, seeking an explanation of the role or benefits of structured public feedback in this specific context. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of structured public feedback in evaluating GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the purpose of algorithmic impact assessment but refers to 'the context' without providing or describing this context within the query. This makes the question unclear for those who do not have access to the unspecified context. For the question to be clear and answerable, it needs to either include the relevant context directly within the question or be framed in a way that does not require external information. Detailing the specific aspects or scenarios of algorithmic impact assessment being referred to could also help clarify the query.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What threat does automatic signature verification software pose to U.S. voters?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the benefits and roles of integrating AI with human agents in customer service, requiring similar depth and breadth of explanation.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key oversight functions involved in the GAI (General Artificial Intelligence) lifecycle. It is clear in its intent, seeking specific information about oversight functions within a defined context (GAI lifecycle). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What key info should designers and devs include for user understanding of automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about issues arising from system flaws in benefit allocation. It is clear in its intent, seeking information on the problems caused by flaws in the benefit allocation system. The question is independent and does not rely on external references or additional context to be understood. It is specific enough to be answerable by someone with knowledge in the domain of benefit allocation systems.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What issues arise from system flaws in benefit allocation?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 1, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Bill of Rights', 'Automated systems', 'Algorithmic discrimination protections', 'Equitable design', 'Independent evaluation and reporting']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of eased access to dangerous content, specifically violent or hateful material. It is clear in its intent to understand the consequences of such access. The question is self-contained and does not rely on external references or unspecified contexts. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are the implications of eased access to dangerous content in relation to violent or hateful material?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Considerations regarding intellectual property when conducting diligence on training data use include assessing risks related to intellectual property and privacy, and examining whether the use of proprietary or sensitive training data is consistent with applicable laws.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to ensure that automated systems are safe and effective?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the requirements for evaluations involving human subjects to ensure their protection. It is clear in its intent, seeking specific information about the criteria or standards that must be met for human subject protection. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What requirements must evaluations involving human subjects meet to ensure human subject protection?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What combined strategies are recommended for mitigating privacy and intellectual property risks associated with AI-generated content?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI risks management', 'Risk response options', 'Model release approaches', 'Information security', 'Harmful bias mitigation']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Prompt injection', 'Indirect prompt injection attacks', 'Data poisoning', 'Intellectual property risks', 'Obscene and degrading content']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of transparency artifacts in ensuring the integrity and security of third-party AI models during deployment and monitoring. It is clear in specifying the topic of interest (transparency artifacts, integrity, security, third-party AI models) and seeks detailed information on their role. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions ask for the essential information that designers and developers should provide to ensure user understanding of automated systems, sharing the same depth, breadth, and requirements for the answer.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The key oversight functions involved in the GAI lifecycle include senior leadership, legal, compliance, and internal evaluation.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when determining model release approaches?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the intellectual property risks associated with GAI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure data privacy and protect individuals from abusive data practices. It is clear in its intent, seeking specific actions or strategies to address data privacy and protection issues. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What measures should be taken to ensure data privacy and protect individuals from abusive data practices?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Safe and effective systems', 'Automated systems', 'Pre-deployment testing', 'Risk identification and mitigation', 'Independent evaluation']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What’s the role of transparency artifacts in securing third-party AI models?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['TEVV metrics', 'Measurement error models', 'GAI system risks', 'Feedback processes', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the threat posed by automatic signature verification software to U.S. voters. It is specific in its focus on a particular technology (automatic signature verification software) and its potential impact on a defined group (U.S. voters). The intent is clear, seeking information on the negative implications or risks associated with this technology. The question is self-contained and does not rely on external references or prior knowledge beyond general understanding of the terms used.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Testing ensures the safety of automated systems before deployment by requiring extensive testing that follows domain-specific best practices, taking into account the specific technology used and the roles of human operators. This testing should mirror real-world conditions and include both automated and human-led testing. Additionally, community input is gathered through consultation during the design and implementation phases, allowing for the identification and mitigation of potential risks that may impact rights and access, particularly for affected communities. Concerns raised during this consultation should be documented and considered in the development process, ensuring that the system is safe and effective based on community feedback.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What potential risks arise from the increased accessibility of violent or hateful content, particularly in relation to the facilitation of CBRN weapon knowledge and the spread of misinformation?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the key components of risk identification and mitigation in the development of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Organizational responsibilities', 'Incident monitoring', 'Document retention policy', 'AI system inventory']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What criteria must be fulfilled in evaluations involving human subjects to ensure their protection while also considering the implications of AI system performance and content transparency?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the problems that arise from hidden criteria changes in benefit allocation systems. It is clear in its intent, seeking information on the negative consequences of such hidden changes. The question is specific and does not rely on external references or additional context to be understood. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the roles of structured public feedback and participatory methods in the evaluation of GAI (General Artificial Intelligence). It is clear in specifying the two elements of interest (structured public feedback and participatory methods) and the context (GAI evaluation). The intent is to understand the impact or contribution of these elements in the evaluation process. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI Risk Management Framework', 'Generative AI', 'Cross-sectoral profile', 'Risk management priorities', 'Large language models']
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of establishing feedback processes for end users and impacted communities in AI system evaluation metrics?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure the safety and effectiveness of automated systems. It is clear in its intent, seeking specific actions or strategies. The question is independent and does not rely on external references or context. However, it could be improved by specifying the type of automated systems (e.g., industrial robots, AI software) to provide more targeted and relevant answers.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What measures should be taken to ensure that automated systems are safe and effective?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for combined strategies to mitigate privacy and intellectual property risks associated with AI-generated content. It is clear in specifying the type of risks (privacy and intellectual property) and the context (AI-generated content), making the intent clear and understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it independent and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What problems stem from hidden criteria changes in benefit allocation systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the role of transparency artifacts in the context of third-party models, focusing on their purpose and role in security, thus sharing the same depth, breadth, and requirements.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What roles do structured public feedback and participatory methods play in GAI evaluation?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of maintaining a document retention policy in relation to GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Risk Management Framework for Generative AI?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for determining model release approaches. It is clear in its intent, seeking information on factors to consider when deciding how to release a model. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI system incidents', 'Organizational risk management', 'Incident response processes', 'Third-party GAI resources', 'Data privacy and localization compliance']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies help with privacy and IP risks in AI content?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the intellectual property risks associated with GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (intellectual property risks) and the context (GAI systems). The question is self-contained and does not rely on external references or additional context to be understood. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the intellectual property risks associated with GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What organizational risk tolerances should be applied to the utilization of third-party GAI resources?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automatic signature verification software threatens to disenfranchise U.S. voters.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for key actions to ensure ethical data collection and prioritization of privacy. It is clear in its intent, seeking specific actions or practices related to data ethics and privacy. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What proactive strategies should be implemented during the design and deployment of automated systems to ensure they are both safe and free from algorithmic discrimination, particularly for underserved communities?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the key components of risk identification and mitigation in the development of automated systems. It is clear in specifying the topic of interest (risk identification and mitigation) and the context (development of automated systems). The intent is straightforward, seeking a list or description of key components, making it understandable and answerable based on the details provided. No additional context or external references are needed to comprehend or respond to the question.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question addresses issues from system flaws in benefit allocation, while the second question focuses on issues from hidden criteria changes in benefit allocation. These are different sources of issues, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear in its intent, asking about the potential risks associated with the increased accessibility of violent or hateful content, specifically in relation to the facilitation of CBRN (Chemical, Biological, Radiological, and Nuclear) weapon knowledge and the spread of misinformation. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The question is specific and seeks a detailed analysis of the risks involved, which can be provided with sufficient domain knowledge.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What key actions ensure your data is collected ethically and your privacy is prioritized?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address risks associated with AI-generated content, the first question specifically focuses on data privacy risks, whereas the second question encompasses both privacy and intellectual property risks, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What role does automated customer service play in enhancing customer care?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the criteria necessary for evaluations involving human subjects to ensure their protection, while also considering the implications of AI system performance and content transparency. It is clear in its intent, specifying the need for criteria related to human subject protection, AI performance, and content transparency. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of establishing feedback processes for end users and impacted communities in the context of AI system evaluation metrics. It is clear in specifying the topic of interest (feedback processes, end users, impacted communities, AI system evaluation metrics) and seeks an explanation of the rationale behind this practice. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What risks come from easier access to violent content, especially regarding CBRN knowledge and misinformation?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of maintaining a document retention policy in relation to GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (document retention policy) and its relation to GAI systems, making the intent clear and specific. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of maintaining a document retention policy in relation to GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI trustworthy characteristics', 'Human-AI Configuration', 'Information Integrity', 'Data Privacy', 'Confabulation']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What criteria ensure human subject protection in AI evaluations?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'When determining model release approaches, considerations should include documenting trade-offs, decision processes, and relevant measurement and feedback results for risks that do not surpass organizational risk tolerance. Additionally, different approaches for model release should be considered, such as leveraging a staged release approach and evaluating release approaches in the context of the model and its projected use cases.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions inquire about the role of public feedback in evaluating GAI systems, sharing the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Risk Management Framework for Generative AI. It is clear in specifying the topic of interest (AI Risk Management Framework for Generative AI) and seeks specific information about its purpose. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures should be taken to address confabulation in GAI system outputs?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the organizational risk tolerances that should be applied to the utilization of third-party GAI (General Artificial Intelligence) resources. It is clear in its intent, seeking specific information on risk tolerances related to a particular context (third-party GAI resources). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks for measures to ensure data privacy and protect against abusive data practices, while the second question is broader, asking about ethical data collection and privacy in general. They differ in depth and breadth.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on general evaluations involving human subjects, while the second specifically addresses AI evaluations. This difference in context leads to different constraints and requirements.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'While both questions address the risks of easier access to harmful content, the first question focuses on violent or hateful material in general, whereas the second question specifically mentions CBRN knowledge and misinformation, leading to different depths and specificities.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 1.75}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Security measures assessment', 'Transparency and accountability risks', 'Intellectual property infringement', 'Digital content transparency solutions', 'Human-AI configuration']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of automated customer service in enhancing customer care. It is clear in its intent, seeking information on the impact or contribution of automated customer service to customer care. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What role does automated customer service play in enhancing customer care?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI risks management', 'Risk response options', 'Model release approaches', 'Information security', 'Harmful bias mitigation']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for proactive strategies to ensure the safety and fairness of automated systems, with a particular focus on avoiding algorithmic discrimination against underserved communities. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking actionable strategies for design and deployment phases.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does a document retention policy play in ensuring the integrity and oversight of GAI systems throughout their lifecycle?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the risks to intellectual property (IP) arising from the use of copyrighted works and data poisoning by generative AI (GAI). It is clear in specifying the topic of interest (risks to IP) and the specific concerns (use of copyrighted works and data poisoning). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Issues arising from hidden criteria changes in benefit allocation include individuals being denied benefits due to data entry errors and other system flaws, which were only revealed when an explanation of the system was demanded. The lack of transparency made it harder for errors to be corrected in a timely manner.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Strategies to help with privacy and intellectual property (IP) risks in AI content include conducting periodic monitoring of AI-generated content for privacy risks, implementing processes for responding to potential intellectual property infringement claims, documenting training data curation policies, establishing policies for collection and retention of data, and conducting appropriate diligence on training data use to assess intellectual property and privacy risks.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The key components of risk identification and mitigation in the development of automated systems include pre-deployment testing, risk identification and mitigation processes, ongoing monitoring, and adherence to domain-specific standards. These components aim to ensure that systems are safe and effective based on their intended use and to mitigate unsafe outcomes, including those beyond the intended use.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What strategies ensure safe, fair automated systems for underserved communities?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the risks associated with transparency and accountability as identified in the MAP function?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the different risk response options identified for high-priority AI risks?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What risks to IP arise from GAI's use of copyrighted works and data poisoning?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of establishing feedback processes for end users and impacted communities in AI system evaluation metrics is to allow these groups to report problems and appeal system outcomes, ensuring that the impact of AI-generated content on different social, economic, and cultural groups is assessed and understood.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to address confabulation in GAI (General Artificial Intelligence) system outputs. It is clear in specifying the issue (confabulation) and the context (GAI system outputs), making the intent straightforward. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the AI Risk Management Framework (AI RMF) for Generative AI is to improve the ability of organizations to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. It assists organizations in deciding how to best manage AI risks in alignment with their goals, legal/regulatory requirements, and best practices.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI model explanation', 'GAI risks', 'Privacy risk assessment', 'Data provenance', 'Human-AI configuration']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What impact do integrated automated customer service systems have on addressing complex customer needs while ensuring human oversight?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 1, 'score': 1.25}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Equitable design', 'Automated systems', 'Legal protections', 'Proactive equity assessments']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on general measures for ensuring the safety and effectiveness of automated systems, while the second question specifically addresses strategies for ensuring that automated systems are safe and fair for underserved communities. The scope and requirements differ.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To ensure ethical data collection and privacy, designers, developers, and deployers of automated systems should seek user permission and respect their decisions regarding data collection, use, access, transfer, and deletion. They should implement built-in protections, ensure data collection conforms to reasonable expectations, and only collect data that is strictly necessary. Consent should be meaningful and understandable, and enhanced protections should be in place for sensitive domains. Additionally, there should be oversight of surveillance technologies to protect privacy and civil liberties.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What actions are suggested for conducting a privacy risk assessment of the AI system?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question broadly asks about intellectual property risks associated with GAI systems, while the second question specifically focuses on IP risks from using copyrighted works and data poisoning. This difference in specificity leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Human alternatives', 'Healthcare navigators', 'Automated customer service', 'Ballot curing laws', 'Human-AI integration']
[ragas.testset.evolutions.INFO] seed question generated: "What role do legal protections play in addressing algorithmic discrimination?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data privacy', 'Identity theft', 'Facial recognition system', 'Surveillance software', 'Employee discussions about union activity']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of a document retention policy in ensuring the integrity and oversight of GAI (General Artificial Intelligence) systems throughout their lifecycle. It is clear in specifying the topic of interest (document retention policy) and the context (integrity and oversight of GAI systems). The intent is also clear, seeking an explanation of the policy's role. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What role does human-AI integration play in enhancing customer service?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the different risk response options identified for high-priority AI risks. It is clear in specifying the topic of interest (risk response options) and the context (high-priority AI risks). The intent is straightforward, seeking a list or description of these options. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the different risk response options identified for high-priority AI risks?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Organizational risk tolerances that should be applied to the utilization of third-party GAI resources include applying risk tolerances to the utilization of third-party datasets and other GAI resources, fine-tuned third-party models, and existing third-party models adapted to a new domain. Additionally, it involves reassessing risk measurements after fine-tuning third-party GAI models.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Eased access to violent content can lead to the production of and access to violent, inciting, radicalizing, or threatening content, as well as recommendations to carry out self-harm or conduct illegal activities. This includes difficulty controlling public exposure to hateful and disparaging or stereotyping content. Additionally, the lowered barrier to generate and support the exchange of content may not distinguish fact from opinion or acknowledge uncertainties, which could be leveraged for large-scale dis- and mis-information campaigns, potentially impacting the operational likelihood of attacks involving CBRN knowledge.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Continuous monitoring of GAI system impacts', 'Harmful bias and homogenization', 'Structured human feedback exercises', 'GAI red-teaming', 'Information integrity']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does a document retention policy support GAI system integrity?"
[ragas.testset.evolutions.INFO] seed question generated: "What are some examples of how data privacy principles aim to protect against identity theft?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Incident response plans', 'Third-party GAI technologies', 'Data privacy', 'Continuous monitoring', 'Vendor contracts']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the impact of integrated automated customer service systems on addressing complex customer needs while ensuring human oversight. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on both the effectiveness of these systems in handling complex needs and the role of human oversight.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of structured human feedback exercises in the context of GAI risk measurement and management?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI system context', 'Harmful bias and homogenization', 'Interdisciplinary AI actors', 'Risk measurement plans', 'Human-AI configuration']
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account regarding data privacy when establishing incident response plans for third-party GAI technologies?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To address confabulation in GAI system outputs, the following measures should be taken: review and verify sources and citations in GAI system outputs during pre-deployment risk measurement and ongoing monitoring activities (MS-2.5-003), and avoid extrapolating GAI system performance or capabilities from narrow, non-systematic, and anecdotal assessments (MS-2.5-001).', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for suggested actions to conduct a privacy risk assessment of an AI system. It is clear in its intent, seeking specific steps or actions related to privacy risk assessment. The question is independent and does not rely on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What actions are suggested for conducting a privacy risk assessment of the AI system?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do automated customer service systems meet complex needs with human oversight?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI technologies', 'Content provenance', 'Synthetic content detection', 'Digital transparency mechanisms', 'Provenance data tracking']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Strategies to ensure safe and fair automated systems for underserved communities include conducting proactive equity assessments during the design phase, using representative and robust data, guarding against proxies that may lead to algorithmic discrimination, and implementing ongoing monitoring and evaluation to confirm protections against algorithmic discrimination. These strategies aim to identify potential discrimination and effects on equity, ensuring that the systems are designed and deployed in an equitable manner.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of legal protections in addressing algorithmic discrimination. It is clear in specifying the topic of interest (legal protections) and the issue it addresses (algorithmic discrimination). The intent is straightforward, seeking an explanation of how legal measures can mitigate or address biases in algorithms. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of legal protections and algorithmic discrimination.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for the purpose of maintaining a document retention policy in relation to GAI systems, while the second question focuses on how such a policy supports GAI system integrity. Although related, the questions have different focuses and thus different depths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of human-AI configuration in the context of risk measurement and management for GAI systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Human subject protection in AI evaluations is ensured through several criteria, including: 1) evaluations involving human subjects must meet applicable requirements and be representative of the relevant population; 2) options must be provided for human subjects to withdraw participation or revoke consent for the use of their data; 3) techniques such as anonymization and differential privacy should be used to minimize risks associated with linking AI-generated content back to individual human subjects; 4) documentation of how content provenance data is tracked and how it interacts with privacy and security is necessary, including the removal of personally identifiable information (PII).', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of content provenance in managing risks associated with AI-generated synthetic content?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of human-AI integration in enhancing customer service. It is clear in its intent, seeking information on how the combination of human and AI efforts can improve customer service. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Intellectual property risks from GAI systems may arise where the use of copyrighted works is not a fair use under the fair use doctrine. If a GAI system’s training data included copyrighted material, GAI outputs displaying instances of training data memorization could infringe on copyright. Additionally, data poisoning poses a risk where an adversary compromises a training dataset used by a model to manipulate its outputs or operation, potentially leading to malicious tampering with data or parts of the model.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking for examples of how data privacy principles protect against identity theft. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking information on the application of data privacy principles in the context of identity theft protection.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What are some examples of how data privacy principles aim to protect against identity theft?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the role of automated customer service in enhancing customer care, while the second question addresses how automated customer service systems meet complex needs with human oversight. These questions have different constraints and requirements, as well as different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about options for addressing high-priority AI risks and their relation to organizational tolerance. It is clear in its intent, seeking information on both the strategies for mitigating AI risks and how these strategies align with an organization's risk tolerance. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Human alternatives', 'Opt-out mechanism', 'Timely human consideration', 'Fallback and escalation system', 'Automated systems', 'Equitable access', 'Timely consideration', 'Human decision-maker', 'Automation bias']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['TEVV metrics', 'Measurement error models', 'GAI system risks', 'Feedback processes', 'Harmful bias and homogenization']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context does not provide specific information on how a document retention policy supports GAI system integrity.', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the AI Safety Institute established by NIST?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The context mentions that algorithmic discrimination may violate legal protections depending on specific circumstances, indicating that legal protections play a role in addressing algorithmic discrimination.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What options exist for addressing high-priority AI risks, and how do they relate to organizational tolerance?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the risks associated with transparency and accountability as identified in the MAP function. It is clear in specifying the topic of interest (risks, transparency, accountability, MAP function) and seeks detailed information on these risks. However, the question assumes familiarity with the 'MAP function' without providing any context or explanation of what it entails. To improve clarity and answerability, the question could benefit from a brief description or definition of the MAP function, or alternatively, frame the question in a way that does not rely on specific, potentially unfamiliar terminology.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What are the risks associated with transparency and accountability as identified in the MAP function?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-generated content', 'Real-time auditing tools', 'User feedback mechanisms', 'Synthetic data', 'Incident response and recovery plans']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of structured human feedback exercises specifically in the context of GAI (General Artificial Intelligence) risk measurement and management. It is clear in specifying the topic of interest (structured human feedback exercises) and the context (GAI risk measurement and management), making the intent clear and the question self-contained. No additional context or external references are needed to understand or answer the question.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of timely human consideration in the context of automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What instances illustrate how principles of data privacy mitigate risks associated with unauthorized data collection and potential identity theft?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the steps that connect AI red-teaming and stakeholder engagement in the context of assessing privacy risks. It is clear in its intent, specifying the two activities (AI red-teaming and stakeholder engagement) and the context (assessing privacy risks). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of creating measurement error models for pre-deployment metrics in the context of TEVV processes?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of using structured feedback mechanisms in relation to AI-generated content?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Human-AI integration plays a key role in enhancing customer service by allowing companies to provide faster customer care through partially automated customer service platforms. These systems help answer customer questions and compile common problems for human agents to review, while maintaining human agents to respond to complicated requests. This integration is viewed as essential for successful customer service.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of content provenance in managing risks associated with AI-generated synthetic content. It is clear in specifying the topic of interest (content provenance) and the context (managing risks associated with AI-generated synthetic content). The intent is clear, seeking an explanation of the importance or role of content provenance in this specific context. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What steps link AI red-teaming and stakeholder engagement in assessing privacy risks?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Information integrity', 'Human-AI configuration', 'Digital content transparency', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Opt out', 'Human alternatives', 'Automated systems', 'Human consideration', 'Sensitive domains']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Automated customer service systems meet complex needs with human oversight by integrating automated services such as chat-bots and AI-driven call response systems, which can escalate issues to a human support team when necessary. This allows companies to provide faster customer care while ensuring that human agents are available to handle complicated requests.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of human-AI configuration in the context of risk measurement and management for GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (human-AI configuration) and the context (risk measurement and management for GAI systems). The intent is to understand the importance or impact of this configuration within the given context. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the significance of human-AI configuration in the context of risk measurement and management for GAI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the significance of human-AI configuration in managing GAI risks and ensuring information integrity?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on identifying risk response options for high-priority AI risks, while the second question asks for options and their link to organizational tolerance, adding an additional layer of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the AI Safety Institute established by NIST. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about the purpose of a specific institute.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of the AI Safety Institute established by NIST?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 2, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Reporting expectations', 'Transparency', 'Artificial Intelligence ethics', 'Traffic calming measures', 'AI Risk Management Framework', 'National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for data privacy when establishing incident response plans for third-party GAI (General Artificial Intelligence) technologies. It is specific in its focus on data privacy and incident response plans, and it clearly identifies the context of third-party GAI technologies. The intent is clear, seeking information on what factors need to be considered. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What considerations should be taken into account regarding data privacy when establishing incident response plans for third-party GAI technologies?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of structured human feedback exercises in the context of GAI risk measurement and management is to define use cases, contexts of use, capabilities, and negative impacts where these exercises would be most beneficial. They are aimed at monitoring and improving outputs, evaluating the quality and integrity of data used in training, and tracking risks or opportunities related to GAI that cannot be measured quantitatively.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 2, 'relevance': 2, 'score': 1.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Artificial Intelligence Decisionmaking', 'Biometric Information Privacy Act', 'Model Cards for Model Reporting', 'Adverse Action Notice Requirements', 'Explainable Artificial Intelligence (XAI)']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the risks associated with transparency and accountability as identified in the MAP function. While it specifies the topic of interest (risks, transparency, accountability, MAP function), it assumes familiarity with what the 'MAP function' refers to without providing any context or explanation. This makes the question unclear for those who do not know what the MAP function is or its relevance to transparency and accountability. To improve clarity and answerability, the question could include a brief description of the MAP function or specify the context in which it is used.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for instances that illustrate how principles of data privacy mitigate risks associated with unauthorized data collection and potential identity theft. It is clear in specifying the topic of interest (principles of data privacy) and the risks it aims to address (unauthorized data collection and identity theft). The intent is clear, seeking specific examples or instances. The question is self-contained and does not rely on external references or prior knowledge not included within the question itself.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the role of the National Institute of Standards and Technology in advancing artificial intelligence?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for specific actions to conduct a privacy risk assessment of an AI system, while the second question focuses on the relationship between AI red-teaming and stakeholder engagement in the context of privacy risk assessment. These questions have different constraints and requirements, as well as different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of creating measurement error models for pre-deployment metrics within the context of TEVV (Test, Evaluation, Verification, and Validation) processes. It is clear in specifying the topic of interest (measurement error models, pre-deployment metrics, TEVV processes) and seeks an explanation of the purpose behind this practice. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of using structured feedback mechanisms in relation to AI-generated content. It is clear in specifying the topic of interest (structured feedback mechanisms) and the context (AI-generated content). The intent is straightforward, seeking an explanation of the purpose or benefits of these mechanisms. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of Model Cards for Model Reporting in the context of artificial intelligence?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of timely human consideration in the context of automated systems. It is clear in specifying the topic of interest (timely human consideration) and the context (automated systems). The intent is to understand the significance of human involvement in automated processes, which is a specific and answerable query. The question is self-contained and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the importance of timely human consideration in the context of automated systems?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What objectives does the U.S. AI Safety Institute aim to achieve in relation to the standards and frameworks for managing AI risks as outlined by NIST?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about considerations for using automated systems in sensitive domains. It is clear in its intent, seeking information on factors to consider, and does not rely on external references or unspecified contexts. The question is specific and independent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What considerations should be taken into account when using automated systems in sensitive domains?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Content provenance is significant in managing risks associated with AI-generated synthetic content as it involves digital transparency mechanisms like provenance data tracking, which can trace the origin and history of content. This helps in distinguishing human-generated content from AI-generated synthetic content, facilitating greater information access about both authentic and synthetic content. Provenance data tracking can assist in assessing authenticity, integrity, intellectual property rights, and potential manipulations in digital content, thereby improving information integrity and upholding public trust.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Participatory engagement methods', 'Field testing', 'AI red-teaming', 'User feedback', 'Risk management', 'Pre-deployment testing', 'GAI system validity', 'Measurement gaps', 'Structured public feedback', 'AI Red-teaming']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the significance of human-AI configuration in managing GAI (General Artificial Intelligence) risks and ensuring information integrity. It is clear in specifying the topic of interest (human-AI configuration) and the aspects it is concerned with (managing risks and ensuring information integrity). The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The options for high-priority AI risks include mitigating, transferring, avoiding, or accepting these risks. Specifically, for risks that do not surpass organizational risk tolerance, it is suggested to document trade-offs, decision processes, and relevant measurement and feedback results. For risks that surpass organizational risk tolerances, the recommended actions are to mitigate, transfer, or avoid those risks.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-enabled systems', 'Technological diffusion', 'Urban planning', 'Criminal justice system', 'Predictive policing']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What examples show how data privacy principles reduce risks of unauthorized data collection and identity theft?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of the National Institute of Standards and Technology (NIST) in advancing artificial intelligence. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks information about NIST's contributions to AI.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the roles of interdisciplinary teams and human-AI configuration in General Artificial Intelligence (GAI) risk management. It is clear in specifying the two elements of interest (interdisciplinary teams and human-AI configuration) and the context (GAI risk management). However, the use of abbreviations like 'GAI' and 'mgmt' might not be immediately clear to all readers. Expanding these abbreviations to 'General Artificial Intelligence' and 'management' would improve clarity. Additionally, specifying what aspects of risk management are of interest (e.g., risk identification, mitigation strategies) could further enhance the question's clarity and answerability.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of field testing in the evaluation of GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.INFO] seed question generated: "What concerns were raised by panelists regarding the use of predictive policing in the criminal justice system?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of using structured feedback mechanisms in relation to AI-generated content is to solicit and capture user input about the content to detect subtle shifts in quality or alignment with community and societal values.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'AI red-teaming and stakeholder engagement connect in privacy risk assessment by engaging directly with end-users and other stakeholders to understand their expectations and concerns regarding content provenance. This feedback is then used to guide the design of provenance data-tracking techniques, which is essential for addressing privacy risks identified during AI red-teaming assessments.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of Model Cards for Model Reporting within the context of artificial intelligence. It is specific, independent, and has a clear intent, making it understandable and answerable based on the details provided. The question does not rely on external references or additional context, and it clearly seeks an explanation of the purpose of Model Cards in AI.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the purpose of Model Cards for Model Reporting in the context of artificial intelligence?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['National Institute of Standards and Technology', 'Artificial intelligence', 'AI Safety Institute', 'Safe and trustworthy AI', '2023 Executive Order on Safe AI', 'AI Risk Management Framework', 'Trustworthy AI', 'Bias in Artificial Intelligence', 'GPT-4 Technical Report', 'Unsafe Diffusion']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of creating measurement error models for pre-deployment metrics in the context of TEVV processes is to demonstrate construct validity for each metric, ensuring that the metric effectively operationalizes the desired concept. This involves measuring or estimating and documenting biases or statistical variance in applied metrics or structured human feedback processes, while leveraging domain expertise when modeling complex societal constructs such as hateful content.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about factors related to data privacy that must be aligned with GAI (General Artificial Intelligence) incident response plans. It is clear in specifying the topic of interest (data privacy factors) and the context (GAI incident response plans). The intent is to understand the alignment between data privacy considerations and incident response plans for GAI, which is specific and understandable without needing additional context. Therefore, the question meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What roles do interdisciplinary teams and human-AI config play in GAI risk mgmt?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the objectives of the U.S. AI Safety Institute in relation to the standards and frameworks for managing AI risks as outlined by NIST. It is specific in mentioning the U.S. AI Safety Institute and NIST, and it clearly seeks information about the objectives related to AI risk management standards and frameworks. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions seek examples of how data privacy principles protect against identity theft, with the second question also mentioning unauthorized data collection. However, the primary focus on identity theft remains consistent, sharing the same depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.INFO] seed question generated: "What are the concerns associated with unsafe diffusion in the context of AI-generated content?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'Disinformation and misinformation', 'Generative AI models', 'Information security risks', 'Cybersecurity attacks']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What factors related to data privacy must be aligned with GAI incident response plans?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['AI-enabled nudification technology', 'Image-based abuse', 'Non-consensual intimate images', 'AI-powered cameras', 'Road safety habits']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the factors that influence the need for human alternatives in automated systems for sensitive areas. It is clear in its intent, seeking information on the influencing factors, and does not rely on external references or unspecified contexts. The question is specific and independent, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What goals does the U.S. AI Safety Institute have for NIST's AI risk standards?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connections between timely human fallback and the effectiveness of automated systems. While it is clear in its intent to explore the relationship between human intervention and system performance, it is somewhat vague in specifying the context or type of automated systems being referred to. To improve clarity and answerability, the question could specify the domain or type of automated systems (e.g., industrial automation, AI-driven customer service) and what is meant by 'timely human fallback' (e.g., specific scenarios or conditions under which human intervention is considered timely).", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What connections exist between timely human fallback and the effectiveness of automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 0 times
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What factors influence the need for human alternatives in automated systems for sensitive areas?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential impacts of disinformation and misinformation facilitated by GAI systems on public trust?"
[ragas.testset.evolutions.INFO] seed question generated: "What problems does AI-enabled nudification technology seek to address and protect against?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of field testing in the evaluation of GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (field testing) and the context (evaluation of GAI systems), making the intent straightforward. The question is self-contained and does not rely on external references or additional context to be understood and answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What is the purpose of field testing in the evaluation of GAI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Algorithmic discrimination', 'Equity assessments', 'Representative data', 'Proactive testing']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role do Model Cards play in ensuring transparency and accountability in AI systems, particularly in light of privacy concerns highlighted by recent data breaches and surveillance practices?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses specifically on the significance of human-AI configuration in risk measurement and management for GAI systems, while the second question broadens the scope to include the role of interdisciplinary teams along with human-AI configuration in GAI risk management. This difference in scope leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Unemployment benefits system', 'Fraud detection system', 'Access to pain medication', 'Automated performance evaluation', 'Human alternatives']
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions focus on data privacy considerations in the context of incident response plans for GAI technologies, requiring similar depth and breadth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The National Institute of Standards and Technology (NIST) develops measurements, technology, tools, and standards to advance reliable, safe, transparent, explainable, privacy-enhanced, and fair artificial intelligence (AI) to realize its full commercial and societal benefits without harm to people or the planet. NIST has conducted both fundamental and applied work on AI for more than a decade and is helping to fulfill the 2023 Executive Order on Safe, Secure, and Trustworthy AI.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The significance of human-AI configuration in managing GAI risks and ensuring information integrity lies in its role in evaluating content lineage and origin, adapting training programs for digital content transparency, developing certification programs for managing GAI risks, delineating human proficiency tests from GAI capabilities, and implementing systems to monitor and track outcomes of human-GAI configurations for future improvements. Involving end-users, practitioners, and operators in prototyping and testing activities is also crucial, especially in various scenarios including crisis situations or ethically sensitive contexts.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of using representative data in the development of automated systems?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': "The first question asks about the purpose of the AI Safety Institute established by NIST, while the second question focuses on the goals of the U.S. AI Safety Institute specifically related to NIST's AI risk standards. These questions have different focuses and requirements.", 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What issues arise from the lack of human alternatives in automated systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the concerns associated with unsafe diffusion in the context of AI-generated content. It is clear in specifying the topic of interest (unsafe diffusion) and the context (AI-generated content), making the intent clear and understandable. The question is self-contained and does not rely on external references or prior knowledge not shared within the question itself. Therefore, it meets the criteria for independence and clear intent.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the considerations for using automated systems in sensitive domains, while the second question focuses on the need for human input in such systems. These are related but distinct inquiries with different depths and requirements.', 'verdict': 0}
[ragas.testset.evolutions.INFO] seed question generated: "What role do civil rights play in the context of automated systems and technology according to the foreword?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 1, 'structure': 1, 'relevance': 1, 'score': 1.0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Automated systems', 'Derived data sources', 'Data reuse limits', 'Independent evaluation', 'Safety and effectiveness']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.0}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Lisa Feldman Barrett', 'Microsoft Corporation', 'National Association for the Advancement of Colored People', 'University of Michigan Ann Arbor', 'OSTP listening sessions', 'OSTP', 'Artificial intelligence', 'Biometric technologies', 'Request For Information (RFI)', 'Public comments']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the problems that AI-enabled nudification technology aims to address and protect against. It is clear in its intent, seeking specific information about the objectives and protective measures of this technology. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential impacts of disinformation and misinformation facilitated by GAI (Generative AI) systems on public trust. It is clear in specifying the topic of interest (disinformation and misinformation by GAI systems) and the specific aspect it seeks to explore (impact on public trust). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the potential impacts of disinformation and misinformation facilitated by GAI systems on public trust?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connections between timely human fallback and the effectiveness of automated systems. While it is clear in its intent to explore the relationship between human intervention and system performance, it is somewhat vague in specifying the context or type of automated systems being referred to. To improve clarity and answerability, the question could specify the domain or type of automated systems (e.g., industrial automation, AI-driven customer service) and what is meant by 'timely human fallback' (e.g., specific scenarios or conditions under which human intervention is considered timely).", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.evolutions.INFO] seed question generated: "What precautions should be taken when using derived data sources in automated systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the role of Model Cards in ensuring transparency and accountability in AI systems, with a specific focus on privacy concerns related to recent data breaches and surveillance practices. It is clear in its intent, specifying the aspect of AI systems (Model Cards) and the context (privacy concerns from data breaches and surveillance). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of using representative data in the development of automated systems. It is clear in its intent, seeking an explanation of why representative data is crucial in this context. The question is independent and does not rely on external references or additional context to be understood. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What is the importance of using representative data in the development of automated systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What actions did the OSTP take to engage with stakeholders regarding the use of artificial intelligence and biometric technologies?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Interdisciplinary teams play a crucial role in GAI risk management by reflecting a wide range of capabilities, competencies, demographic groups, domain expertise, educational backgrounds, lived experiences, professions, and skills. Their participation is documented, and opportunities for interdisciplinary collaboration are prioritized. Additionally, human-AI configuration is important as it addresses harmful bias and homogenization, ensuring that data or benchmarks used in risk measurement are representative of diverse in-context user populations.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the connections between field testing, user feedback, and GAI (General Artificial Intelligence) system evaluation. It is clear in its intent to understand the relationships among these three aspects. The question is independent and does not rely on external references or unspecified contexts, making it self-contained. However, it could benefit from specifying what kind of connections are of interest (e.g., methodological, impact on performance, user satisfaction) to further refine the scope of the answer.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How do Model Cards enhance AI transparency and accountability amid privacy issues?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Cyberattacks', 'Intellectual Property', 'Obscene and abusive content', 'CBRN weapons', 'Chemical and biological design tools']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the issues that arise from the lack of human alternatives in automated systems. It is clear in its intent, seeking information on the potential problems or challenges associated with relying solely on automated systems without human intervention or alternatives. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What issues arise from the lack of human alternatives in automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the concerns raised by panelists regarding the use of predictive policing in the criminal justice system. It is clear in specifying the topic of interest (concerns about predictive policing) and the context (criminal justice system). The intent is straightforward, seeking information on the specific concerns discussed by panelists. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] simple question generated: "What concerns were raised by panelists regarding the use of predictive policing in the criminal justice system?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data Privacy', 'Privacy Act of 1974', 'NIST Privacy Framework', 'Biometric identifying technology', 'Workplace surveillance']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Algorithmic discrimination', 'Automated systems', 'Bias testing', 'Equitable design', 'Systemic biases']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The need for human input in sensitive automated systems is driven by the requirement for timely human consideration and remedy when automated systems fail, produce errors, or when individuals wish to appeal or contest the impacts of these systems. Additionally, human input is necessary to ensure that automated systems are tailored to their intended purpose, provide meaningful access for oversight, and incorporate human consideration for adverse or high-risk decisions.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between field testing, user feedback, and GAI system evaluation?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential impacts of increased attack surfaces for cyberattacks on system availability and data integrity?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Decommissioning AI systems', 'GAI risks', 'Roles and responsibilities', 'Incident response procedures', 'Data security and retention']
[ragas.testset.evolutions.INFO] seed question generated: "What are some real-life examples of how data privacy principles can be implemented through laws and policies?"
[ragas.testset.evolutions.INFO] seed question generated: "What measures are being taken to ensure equitable design in automated systems to protect against algorithmic discrimination?"
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What role does the inclusion of diverse and representative data play in ensuring that automated systems are free from algorithmic bias and meet the expectations for equitable outcomes?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 2, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Unemployment benefits system', 'Fraud detection system', 'Access to pain medication', 'Automated performance evaluation', 'Human alternatives']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for precautions to be taken when using derived data sources in automated systems. It is clear in specifying the topic of interest (precautions, derived data sources, automated systems) and seeks detailed information on safety or best practices. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the purpose of Model Cards in AI, while the second question focuses on how Model Cards enhance AI transparency and accountability, specifically amid privacy issues. These questions have different constraints and requirements, leading to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'AI-enabled nudification technology seeks to address and protect against image-based abuse, particularly the creation of non-consensual intimate images that disproportionately impact women. It aims to combat the proliferation of apps that allow users to create or alter images of individuals without their consent, which can lead to devastating harm to victims.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What is the importance of documenting roles and responsibilities related to managing AI risks within an organization?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI lifecycle', 'AI technology risks', 'Organizational practices for AI', 'Impact documentation process', 'Content provenance methodologies']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of civil rights in the context of automated systems and technology, specifically according to 'the foreword'. While it is clear in specifying the topic of interest (civil rights, automated systems, technology) and the source of information (the foreword), it assumes access to and understanding of 'the foreword' without providing its content or context. This makes the question unclear for those without direct access to the foreword. To improve clarity and answerability, the question could include a brief description or key points from the foreword, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] rewritten question: "What role do civil rights play in the context of automated systems and technology according to the foreword?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the actions taken by the OSTP to engage with stakeholders concerning the use of artificial intelligence and biometric technologies. It is specific in identifying the organization (OSTP) and the topics of interest (artificial intelligence and biometric technologies). The intent is clear, seeking information on stakeholder engagement activities. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the connections between GAI-facilitated misinformation and the erosion of public trust. It is clear in specifying the two concepts of interest (GAI-facilitated misinformation and public trust erosion) and seeks to understand the relationship between them. The intent is clear, and the question is independent as it does not rely on external references or unspecified contexts. However, it could be improved by specifying what 'GAI' stands for to ensure clarity for all readers.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential issues associated with automated performance evaluation in the workplace?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about the purpose of field testing in the evaluation of GAI systems, while the second question asks about the relationship between field testing, user feedback, and GAI evaluation. The second question has a broader scope and different requirements.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question generated: "What ethical and operational concerns did panelists express regarding the implications of AI-driven predictive policing on community safety and democratic values?"
[ragas.testset.evolutions.INFO] seed question generated: "What is the purpose of the impact documentation process in the context of GAI systems?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What connections exist between GAI-facilitated misinformation and public trust erosion?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for real-life examples of how data privacy principles can be implemented through laws and policies. It is clear in its intent, specifying that it seeks examples and focusing on the implementation of data privacy principles through legal and policy measures. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are some real-life examples of how data privacy principles can be implemented through laws and policies?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential impacts of increased attack surfaces for cyberattacks on system availability and data integrity. It is clear in specifying the topic of interest (increased attack surfaces, cyberattacks) and the aspects to be addressed (system availability, data integrity). The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the potential impacts of increased attack surfaces for cyberattacks on system availability and data integrity?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Trustworthy AI', 'Transparency policies', 'Risk management activities', 'Information integrity', 'GAI capabilities']
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the problems that arise when automated systems do not have options for human review. It is clear in its intent, seeking information on the potential issues or drawbacks of such systems. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The answer to given question is not present in context', 'verdict': -1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about measures being taken to ensure equitable design in automated systems to protect against algorithmic discrimination. It is clear in its intent, specifying the focus on 'equitable design' and 'algorithmic discrimination'. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.", 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question is clear and specific, asking about the role of diverse and representative data in preventing algorithmic bias and achieving equitable outcomes in automated systems. It does not rely on external references or unspecified contexts, making it self-contained and understandable. The intent is clear, seeking an explanation of the impact of data diversity on algorithmic fairness.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What problems emerge when automated systems lack human review options?"
[ragas.testset.evolutions.INFO] seed question generated: "What factors should be considered when evaluating the risk-relevant capabilities of GAI?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the importance of documenting roles and responsibilities related to managing AI risks within an organization. It is clear in its intent, seeking an explanation of the significance of this documentation. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the impact of misinformation facilitated by GAI systems on public trust. They share the same constraints and requirements, and the depth and breadth of the inquiry are similar.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about potential issues associated with automated performance evaluation in the workplace. It is clear in its intent, seeking information on the drawbacks or challenges of using automated systems for performance evaluation. The question is specific and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "How does diverse data help prevent algorithmic bias in automated systems?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Precautions that should be taken when using derived data sources in automated systems include careful tracking and validation of derived data, as it may be high-risk and could lead to feedback loops, compounded harm, or inaccurate results. Such data should be validated against the risk of collateral consequences.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Technology and democracy', 'Automated systems', 'Civil rights', 'AI Bill of Rights', 'Bias and discrimination']
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Field testing, user feedback, and GAI evaluation are linked through structured public feedback mechanisms that assess how GAI systems perform in real-world conditions. Field testing evaluates risks and impacts in controlled settings, while user feedback, gathered through participatory engagement methods, helps organizations understand user interactions and experiences with AI-generated information. Together, these approaches inform the design, implementation, and governance of GAI systems.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks about the role of civil rights in the context of automated systems and technology, specifically according to 'the foreword'. While it is clear in specifying the topic of interest (civil rights, automated systems, technology) and the source of information (the foreword), it assumes access to and understanding of 'the foreword' without providing its content or context. This makes the question unclear for those without direct access to the foreword. To improve clarity and answerability, the question could include a brief description or key points from the foreword, or alternatively, frame the question in a way that does not rely on specific, unpublished documents.", 'verdict': 0}
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'Both questions address the problems that occur when human involvement is missing in automated systems, sharing the same constraints, requirements, and depth of inquiry.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] evolution_filter failed, retrying with 1
[ragas.testset.evolutions.INFO] retrying evolution: 1 times
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the ethical and operational concerns expressed by panelists regarding AI-driven predictive policing and its implications on community safety and democratic values. It is clear in specifying the topic of interest (ethical and operational concerns, AI-driven predictive policing) and the context (community safety and democratic values). The intent is clear, and the question is self-contained, not relying on external references or unspecified contexts. Therefore, it is understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the purpose of the impact documentation process specifically in the context of GAI (General Artificial Intelligence) systems. It is clear in specifying the topic of interest (impact documentation process) and the context (GAI systems), making the intent of the question straightforward. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Environmental impacts of GAI', 'Harmful bias in AI systems', 'Generative AI energy consumption', 'Disparities in model performance', 'Trustworthy AI characteristics']
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of bias and discrimination in automated systems on the rights of the American public?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'OSTP engaged with stakeholders regarding the use of artificial intelligence and biometric technologies by conducting two listening sessions for members of the public, which drew upwards of 300 participants. Additionally, OSTP created an email address (ai-equity@ostp.eop.gov) to solicit comments from the public on the use of artificial intelligence and issued a Request For Information (RFI) on the use and governance of biometric technologies to understand their extent, variety, and the stakeholders impacted by their use or regulation.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses on the general importance of using representative data, while the second question specifically addresses how diverse data helps prevent algorithmic bias. These questions have different depths and specific focuses.', 'verdict': 0}
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Cyberattacks', 'Intellectual Property', 'Obscene and abusive content', 'CBRN weapons', 'Chemical and biological design tools']
[ragas.testset.evolutions.DEBUG] [MultiContextEvolution] multicontext question compressed: "What concerns did panelists raise about AI in policing and its impact on safety and democracy?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for the connection between increased attack surfaces and the risks to system availability and data integrity. It is clear in its intent, seeking an explanation of the relationship between these concepts. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The importance of documenting roles and responsibilities related to managing AI risks within an organization is to ensure that these roles and lines of communication are clear to individuals and teams throughout the organization. This clarity helps in mapping, measuring, and managing AI risks effectively.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for laws and frameworks that illustrate data privacy principles in practice and their implications. It is clear in its intent, seeking specific examples of legal and regulatory frameworks related to data privacy and their practical implications. The question is independent and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': "The question asks for factors to consider when evaluating the risk-relevant capabilities of GAI (General Artificial Intelligence). It is clear in its intent, seeking specific factors related to risk evaluation. The question is independent and does not rely on external references or unspecified contexts. However, it could benefit from a brief clarification of what is meant by 'risk-relevant capabilities' to ensure a comprehensive understanding for all readers.", 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What are the environmental impacts associated with the energy consumption of generative AI systems?"
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['GAI systems', 'AI Actors', 'Unanticipated impacts', 'Information integrity', 'Content provenance']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 2, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.5}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Training data use', 'Intellectual property', 'Data privacy risks', 'Content provenance', 'Generative AI (GAI) risks', 'GAI systems', 'Information integrity', 'Human-AI configuration', 'Digital content transparency', 'Harmful bias and homogenization']
[ragas.testset.filters.DEBUG] context scoring: {'clarity': 1, 'depth': 3, 'structure': 2, 'relevance': 3, 'score': 2.25}
[ragas.testset.evolutions.DEBUG] keyphrases in merged node: ['Data protection', 'Third-party considerations', 'Risk management', 'Pre-deployment testing', 'GAI systems']
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What links increased attack surfaces to risks for system availability and data integrity?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the potential risks associated with the production and access to obscene and abusive content?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What laws and frameworks illustrate data privacy principles in practice and their implications?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The potential issues associated with automated performance evaluation in the workplace include workers being fired by an automated system without the possibility of human review, appeal, or other forms of recourse.', 'verdict': 1}
[ragas.testset.evolutions.INFO] seed question generated: "What measures are suggested to ensure information integrity in the context of AI systems?"
[ragas.testset.evolutions.INFO] seed question generated: "What are the implications of using GAI systems for organizations in terms of risk management and compliance?"
[ragas.testset.evolutions.INFO] seed question generated: "What should be assessed to understand data privacy risks in the use of training data?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Many companies, non-profits, and federal government agencies are taking steps to ensure the public is protected from algorithmic discrimination. Some companies have instituted bias testing as part of their product quality assessment and launch procedures, which has led to changes or the prevention of product launches to avoid public harm. Federal government agencies are developing standards and guidance for the use of automated systems to help prevent bias. Non-profits and companies have created best practices for audits and impact assessments to identify potential algorithmic discrimination and provide transparency in mitigating such biases.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question focuses specifically on predictive policing within the criminal justice system, while the second question addresses broader concerns about AI in policing, including its impact on safety and democracy. These differences lead to varying depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of bias and discrimination in automated systems on the rights of the American public. It is specific in its focus on bias and discrimination within automated systems and their impact on a particular group (the American public). The intent is clear, seeking an explanation of the consequences of these issues on public rights. The question is self-contained and does not rely on external references or prior knowledge beyond a general understanding of automated systems and public rights.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the potential risks associated with the production and access to obscene and abusive content. It is clear in its intent, seeking information on the risks involved. The question is self-contained and does not rely on external references or additional context to be understood. Therefore, it meets the criteria of independence and clear intent.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the potential risks associated with the production and access to obscene and abusive content?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks for real-life examples of data privacy principles implemented through laws and policies, requiring a broader and more detailed response. The second question is more specific, asking only about laws, which narrows the scope and depth of the inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question specifically asks about the impacts of increased attack surfaces on system availability and data integrity, requiring detailed analysis. The second question is broader, asking about the connection between attack surfaces and system/data risks without specifying the impacts or particular aspects like availability and integrity.', 'verdict': 0}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the environmental impacts associated with the energy consumption of generative AI systems. It is specific in its focus on environmental impacts and energy consumption, and it is clear in its intent to understand the consequences of using generative AI systems from an environmental perspective. The question is self-contained and does not rely on external references or additional context to be understood or answered. Therefore, it meets the criteria for clarity and answerability.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the environmental impacts associated with the energy consumption of generative AI systems?"
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks for measures to ensure information integrity in the context of AI systems. It is clear in specifying the topic of interest (information integrity) and the context (AI systems), making the intent clear and understandable. The question is self-contained and does not rely on external references or unspecified contexts, making it independent and answerable based on domain knowledge.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks what should be assessed to understand data privacy risks in the use of training data. It is clear in its intent, seeking specific information on the factors or criteria that need to be evaluated to understand data privacy risks. The question is independent and does not rely on external references or additional context, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the implications of using GAI (General Artificial Intelligence) systems for organizations, specifically focusing on risk management and compliance. It is clear in its intent, seeking information on the potential risks and compliance issues associated with GAI systems in an organizational context. The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] simple question generated: "What are the implications of using GAI systems for organizations in terms of risk management and compliance?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The implications of bias and discrimination in automated systems on the rights of the American public include limiting opportunities, preventing access to critical resources or services, and reflecting or reproducing existing unwanted inequities. These outcomes can threaten people's opportunities, undermine their privacy, and lead to pervasive tracking of their activity, often without their knowledge or consent.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "The context discusses increased attack surfaces for targeted cyberattacks, which may compromise a system's availability or the confidentiality or integrity of training data, code, or model weights. This connection indicates that as attack surfaces increase, the risks to systems and data also escalate.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The purpose of the impact documentation process in the context of GAI systems is to document the risks and potential impacts of the AI technology designed, developed, deployed, evaluated, and used, and to communicate about these impacts more broadly.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the harms arising from easy access to obscene content and its production methods. It is clear in its intent, seeking information on the negative consequences associated with both the consumption and production of obscene content. The question is self-contained and does not rely on external references or prior knowledge, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the energy activities of GAI (General Artificial Intelligence) systems that lead to significant carbon emissions. It is clear in specifying the subject (GAI systems) and the aspect of interest (energy activities leading to carbon emissions). The intent is to understand which specific activities within GAI systems contribute to carbon emissions, making it specific and independent. No additional context or external references are needed to understand or answer the question.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': "Panelists raised concerns about the validity of AI systems used in policing, noting that adverse or irrelevant data can lead to a replication of unjust outcomes. They highlighted issues such as confirmation bias and the tendency to defer to potentially inaccurate automated systems. The impact of these systems on individuals and communities is seen as potentially severe, with concerns that they lack individualization, undermine the belief in people's ability to change for the better, and can lead to job loss and custody issues. Additionally, surveillance technologies can create chilling effects in communities and send negative signals about how community members are viewed. Panelists emphasized that while transparency is important, it is not sufficient for achieving accountability.", 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What harms arise from easy access to obscene content and its production methods?"
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What energy activities of GAI systems lead to significant carbon emissions?"
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'To understand data privacy risks in the use of training data, it is important to conduct appropriate diligence on training data use to assess intellectual property and privacy risks, including examining whether the use of proprietary or sensitive training data is consistent with applicable laws.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Suggested measures to ensure information integrity in the context of AI systems include employing methods to trace the origin and modifications of digital content, integrating tools designed to analyze content provenance and detect data anomalies, verifying the authenticity of digital signatures, and identifying patterns associated with misinformation or manipulation. Additionally, it is recommended to disaggregate evaluation metrics by demographic factors to identify discrepancies in how content provenance mechanisms work across diverse populations.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Diverse data helps prevent algorithmic bias in automated systems by ensuring that any data used in system development or assessment is representative of local communities based on the planned deployment setting. This data should be reviewed for bias considering the historical and societal context, and it should be sufficiently robust to identify and mitigate biases and potential harms.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The Privacy Act of 1974 exemplifies data privacy principles in action by requiring privacy protections for personal information in federal records systems, including limits on data retention and providing individuals a general right to access and correct their data. Additionally, federal law mandates employers to report the costs of surveilling employees during labor disputes, which serves as a transparency mechanism to protect worker organizing.', 'verdict': 1}
[ragas.testset.filters.DEBUG] filtered question: {'feedback': 'The question asks about the risk controls organizations should consider when using third-party Generative AI (GAI) systems for compliance. It is clear in its intent, specifying the context (third-party GAI systems) and the focus (risk controls for compliance). The question is self-contained and does not rely on external references or unspecified contexts, making it understandable and answerable based on the details provided.', 'verdict': 1}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question addresses both production and access to obscene and abusive content, while the second question focuses solely on the issues arising from easy access to obscene content. This difference in scope leads to different depths and breadths of inquiry.', 'verdict': 0}
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the overall environmental impacts of energy consumption in generative AI systems, which is broader. The second question specifically focuses on which activities within generative AI contribute most to carbon emissions, which is narrower in scope.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Factors to consider when evaluating the risk-relevant capabilities of GAI include abuses and impacts to information integrity, dependencies between GAI and other IT or data systems, harm to fundamental rights or public safety, presentation of obscene, objectionable, offensive, discriminatory, invalid or untruthful output, psychological impacts to humans (e.g., anthropomorphization, algorithmic aversion, emotional entanglement), possibility for malicious use, whether the system introduces significant new security vulnerabilities, anticipated system impact on some groups compared to others, and unreliable decision-making capabilities, validity, adaptability, and variability of GAI system performance over time.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] [ReasoningEvolution] question compressed: "What risk controls should orgs consider when using third-party GAI systems for compliance?"
[ragas.testset.filters.DEBUG] evolution filter: {'reason': 'The first question asks about the implications of using GAI systems for organizations in terms of risk management and compliance, which is broader and requires a more detailed analysis. The second question is more specific, focusing only on risk controls for third-party GAI in compliance.', 'verdict': 0}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Easy access to obscene content can lead to the production of and access to obscene, degrading, and/or abusive imagery, which can cause harm, including synthetic child sexual abuse material (CSAM) and nonconsensual intimate images (NCII) of adults.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'The GAI activities that contribute most to carbon emissions include training, maintaining, and operating GAI systems, particularly during the pre-training, fine-tuning, and inference stages. Current estimates suggest that training a single transformer LLM can emit as much carbon as 300 round-trip flights between San Francisco and New York.', 'verdict': 1}
[ragas.testset.evolutions.DEBUG] answer generated: {'answer': 'Organizations can apply standard or existing risk controls and processes to proprietary or open-source GAI technologies, data, and third-party service providers, including acquisition and procurement due diligence, requests for software bills of materials (SBOMs), application of service level agreements (SLAs), and statement on standards for attestation engagement (SSAE) reports to help with third-party transparency and risk management for GAI systems.', 'verdict': 1}