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.

在当前人工智能时代，本研究讨论了负责任的机器学习数据集的重要性，并提出了一个评估数据集的负责任框架。通过公平性、隐私保护和合规性等方面的考虑，我们分析了超过 100 个数据集，发现没有一个数据集能免于公平性、隐私保护和合规性问题。我们对数据集的文档化提供了改进建议，并认为在全球范围内的数据保护法规定下，科学界的数据集创建方法需要修订。