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Artificial Intelligence (AI) has made its way into various scientific fields,providing astonishing improvements over existing algorithms for a wide varietyof tasks. In recent years, there have been severe concerns over thetrustworthiness of AI technologies. The scientific community has focused on thedevelopment of trustworthy AI algorithms. However, machine and deep learningalgorithms, popular in the AI community today, depend heavily on the data usedduring their development. These learning algorithms identify patterns in thedata, learning the behavioral objective. Any flaws in the data have thepotential to translate directly into algorithms. In this study, we discuss theimportance of Responsible Machine Learning Datasets and propose a framework toevaluate the datasets through a responsible rubric. While existing work focuseson the post-hoc evaluation of algorithms for their trustworthiness, we providea framework that considers the data component separately to understand its rolein the algorithm. We discuss responsible datasets through the lens of fairness,privacy, and regulatory compliance and provide recommendations for constructingfuture datasets. After surveying over 100 datasets, we use 60 datasets foranalysis and demonstrate that none of these datasets is immune to issues offairness, privacy preservation, and regulatory compliance. We providemodifications to the ``datasheets for datasets" with important additions forimproved dataset documentation. With governments around the world regularizingdata protection laws, the method for the creation of datasets in the scientificcommunity requires revision. We believe this study is timely and relevant intoday's era of AI.

On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms