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Description: In this study, we discuss the importance of Responsible Machine Learning Datasets through the lens of fairness, privacy, and regulatory compliance and present a large audit of Computer Vision datasets. The audit is conducted through evaluation of the proposed responsible rubric. After surveying over 100 datasets, our detailed analysis of 60 distinct datasets highlights a universal susceptibility to fairness, privacy, and regulatory compliance issues. Please cite the paper below. Mittal, Surbhi, Kartik Thakral, Richa Singh, Mayank Vatsa, Tamar Glaser, Cristian Canton Ferrer, Tal Hassner. "On Responsible Machine Learning Datasets Emphasizing Fairness Privacy and Regulatory Norms with Examples in Biometrics and Healthcare." Nature Machine Intelligence (2024). @article{mittal2024responsible, title={On Responsible Machine Learning Datasets Emphasizing Fairness Privacy and Regulatory Norms with Examples in Biometrics and Healthcare}, author={Mittal, Surbhi, and Thakral, Kartik and Singh, Richa and Vatsa, Mayank and Glaser, Tamar and Ferrer, Cristian Canton and Hassner, Tal}, journal={Nature Machine Intelligence}, year={2024}, publisher={Nature Publishing Group UK London} }