A Taxonomy of Challenges to Curating Fair Datasets

Dora Zhao, Morgan Scheuerman, Pooja Chitre, Jerone Andrews, Georgia Panagiotidou, Shawn Walker, Kathleen Pine, Alice Xiang

Oral ⋅Oral Session 6B: Safety, New Data

Abstract

Despite extensive efforts to create fairer machine learning (ML) datasets, there remains a limited understanding of the practical aspects of dataset curation. Drawing from interviews with 30 ML dataset curators, we present a comprehensive taxonomy of the challenges and trade-offs encountered throughout the dataset curation lifecycle. Our findings underscore overarching issues within the broader fairness landscape that impact data curation. We conclude with recommendations aimed at fostering systemic changes to better facilitate fair dataset curation practices.