Data and its (dis)contents: A survey of dataset development and use in machine learning research

• Published in: Patterns (New York, N.Y.) Vol. 2; no. 11; p. 100336
• Main Authors: Paullada, Amandalynne, Raji, Inioluwa Deborah, Bender, Emily M., Denton, Emily, Hanna, Alex
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 12.11.2021; Elsevier
• Subjects: datasets machine learning; Review; datasets machine learning
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2021.100336

In this work, we survey a breadth of literature that has revealed the limitations of predominant practices for dataset collection and use in the field of machine learning. We cover studies that critically review the design and development of datasets with a focus on negative societal impacts and poor outcomes for system performance. We also cover approaches to filtering and augmenting data and modeling techniques aimed at mitigating the impact of bias in datasets. Finally, we discuss works that have studied data practices, cultures, and disciplinary norms and discuss implications for the legal, ethical, and functional challenges the field continues to face. Based on these findings, we advocate for the use of both qualitative and quantitative approaches to more carefully document and analyze datasets during the creation and usage phases. Datasets form the basis for training, evaluating, and benchmarking machine learning models and have played a foundational role in the advancement of the field. Furthermore, the ways in which we collect, construct, and share these datasets inform the kinds of problems the field pursues and the methods explored in algorithm development. In this work, we survey recent issues pertaining to data in machine learning research, focusing primarily on work in computer vision and natural language processing. We summarize concerns relating to the design, collection, maintenance, distribution, and use of machine learning datasets as well as broader disciplinary norms and cultures that pervade the field. We advocate a turn in the culture toward more careful practices of development, maintenance, and distribution of datasets that are attentive to limitations and societal impact while respecting the intellectual property and privacy rights of data creators and data subjects. Datasets have become a critical component in the advancement of machine learning research. The ways in which such datasets are collected, constructed, and shared play a significant role in shaping the quality and impact of this research. We conduct a survey of the literature on concerns relating to the design, collection, maintenance, and distribution of machine learning datasets, as well as broader disciplinary norms and cultures that pervade the field.