On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare

Abstract

Abstract Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains, catalysing unparalleled enhancements across a broad spectrum of tasks; however, its integrity and trustworthiness have emerged as notable concerns. The scientific community has focused on the development of trustworthy AI algorithms; however, machine learning and deep learning algorithms, popular in the AI community today, intrinsically rely on the quality of their training data. These algorithms are designed to detect patterns within the data, thereby learning the intended behavioural objectives. Any inadequacy in the data has the potential to translate directly into algorithms. 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. Despite the ubiquity of fairness and privacy challenges across diverse data domains, current regulatory frameworks primarily address human-centric data concerns. We therefore focus our discussion on biometric and healthcare datasets, although the principles we outline are broadly applicable across various domains. 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. This finding emphasizes the urgent need for revising dataset creation methodologies within the scientific community, especially in light of global advancements in data protection legislation. We assert that our study is critically relevant in the contemporary AI context, offering insights and recommendations that are both timely and essential for the ongoing evolution of AI technologies.

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