Update README.md

README.md

@@ -230,7 +230,9 @@ To note: not intense tuning, epochs, etc. Still, good?? PAWS-X: weird (large dif
 
 ## Bias and ethics
 
-Bananas
+While a rigorous analysis of our models and datasets for bias was out of the scope of our project (given the very tight schedule and our lack of experience on JAX/FLAX), this issue has still played an important role in our motivation. Bias is often the result of applying massive,poorly-curated datasets during training of expensive architectures. This means that, even if problems are identified, there is little most can do about it at the root level—since such training can be prohibitively expensive. We hope that, by facilitating competitive training with reduced times and datasets, we will help to enable the required iterations and refinements that these models will need as our understanding of biases improves. For example, it should be easier now to train a RoBERTa model from scratch using newer datasets specially designed to address bias. This is surely an exciting prospect, and we hope that this work will contribute in this challenge.
+
+Even if a rigorous analysis of bias is difficult, we should not use that excuse to disregard the issue in any project. Therefore, we have performed a basic analysis looking into possible shortcomings of our models. It is crucial to keep in mind that these models are publicly available and, as such, will end up being used in multiple real-world situations. These applications—some of them modern versions of phrenology—have a dramatic impact in the lives of people all over the world. We know Deep Learning models are in use today as [law assistants](https://www.wired.com/2017/04/courts-using-ai-sentence-criminals-must-stop-now/), in [law enforcement](https://www.washingtonpost.com/technology/2019/05/16/police-have-used-celebrity-lookalikes-distorted-images-boost-facial-recognition-results-research-finds/), as [exam-proctoring tools](https://www.wired.com/story/ai-college-exam-proctors-surveillance/) (also [this](https://www.eff.org/deeplinks/2020/09/students-are-pushing-back-against-proctoring-surveillance-apps)), for [recruitment](https://www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning-algorithm-increasingly-decides-whether-you-deserve-job/) (also [this](https://www.technologyreview.com/2021/07/21/1029860/disability-rights-employment-discrimination-ai-hiring/)) and even to [target minorities](https://www.insider.com/china-is-testing-ai-recognition-on-the-uighurs-bbc-2021-5). Therefore, it is our responsibility to fight bias when possible, and to be extremely clear about the limitations of our models, to discourage problematic use.
 
 ## Analysis