Responsible Machine Learning Datasets

On Responsible Machine Learning Datasets Emphasizing Fairness Privacy and Regulatory Norms with Examples in Biometrics and Healthcare

"This study investigates various computer vision datasets used for machine learning. The fairness, privacy, and regulatory concerns pervading these datasets call for a revision of dataset creation methods to ensure responsible AI development."

Nature Machine Intelligence 2024

Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains, catalyzing unparalleled enhancements across a broad spectrum of tasks, however, its integrity and trustworthiness have emerged as significant 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 behavioral 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 a \textit{responsible rubric} calculated using the proposed framework. 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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The code and data for this research can be accessed here.

This database is available only for research and educational purpose and not for any commercial use. If you use the database in any publications or reports, you must refer to the following paper:

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} }