Post
🔍 Today's pick in Interpretability & Analysis of LMs: Black-Box Access is Insufficient for Rigorous AI Audits by
@stecas
@carsonezell
et al.
Audits conducted on AI systems can identify potential risks and ensure their compliance to safety requirements. Authors categorise audits based on the access to model-related resources (black, grey, white and out-of-the box) and highlight how levels of transparency on audited AI system enable broader and more effective auditing procedures. Technical, physical, and legal safeguards for performing audits are also introduced to ensure minimal security risks for audited companies. Authors conclude that transparency on the type of auditors’ access and methods is a pre-requisite to correctly interpret audit results, and white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.
📄 Paper: Black-Box Access is Insufficient for Rigorous AI Audits (2401.14446)
🔍 Further readings:
📄Taxonomy of AI system access: https://bit.ly/struct-access
💻An API for transparent science on Black-box AI (NNsight): https://nnsight.net/about
Audits conducted on AI systems can identify potential risks and ensure their compliance to safety requirements. Authors categorise audits based on the access to model-related resources (black, grey, white and out-of-the box) and highlight how levels of transparency on audited AI system enable broader and more effective auditing procedures. Technical, physical, and legal safeguards for performing audits are also introduced to ensure minimal security risks for audited companies. Authors conclude that transparency on the type of auditors’ access and methods is a pre-requisite to correctly interpret audit results, and white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.
📄 Paper: Black-Box Access is Insufficient for Rigorous AI Audits (2401.14446)
🔍 Further readings:
📄Taxonomy of AI system access: https://bit.ly/struct-access
💻An API for transparent science on Black-box AI (NNsight): https://nnsight.net/about