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

TL;DR

This paper emphasizes the importance of responsible datasets in AI, proposing a framework to evaluate datasets for fairness, privacy, and compliance, and analyzing over 60 datasets revealing widespread issues.

Contribution

It introduces a responsible dataset evaluation framework considering fairness, privacy, and regulations, and provides recommendations for better dataset construction and documentation.

Findings

None of the surveyed datasets fully comply with fairness, privacy, and regulatory standards.

Proposes modifications to datasheets for datasets for improved documentation.

Highlights the need for revised dataset creation methods due to legal regulations.

Abstract

Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing improvements over existing algorithms for a wide variety of tasks. In recent years, there have been severe concerns over the trustworthiness of AI technologies. The scientific community has focused on the development of trustworthy AI algorithms. However, machine and deep learning algorithms, popular in the AI community today, depend heavily on the data used during their development. These learning algorithms identify patterns in the data, learning the behavioral objective. Any flaws in the data have the potential to translate directly into algorithms. In this study, we discuss the importance of Responsible Machine Learning Datasets and propose a framework to evaluate the datasets through a responsible rubric. While existing work focuses on the post-hoc evaluation of algorithms for their trustworthiness, we provide a framework that considers the data component separately to understand its role in the algorithm. We discuss responsible datasets through the lens of fairness, privacy, and regulatory compliance and provide recommendations for constructing future datasets. After surveying over 100 datasets, we use 60 datasets for analysis and demonstrate that none of these datasets is immune to issues of fairness, privacy preservation, and regulatory compliance. We provide modifications to the ``datasheets for datasets" with important additions for improved dataset documentation. With governments around the world regularizing data protection laws, the method for the creation of datasets in the scientific community requires revision. We believe this study is timely and relevant in today's era of AI.