A Taxonomy of Challenges to Curating Fair Datasets
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.
Authors
- Dora Zhao*
- Morgan Klaus Scheuerman
- Pooja Chitre*
- Jerone Andrews
- Georgia Panagiotidou*
- Shawn Walker*
- Kathleen H. Pine*
- Alice Xiang
*External Authors
Venue
NeurIPS 2024
Date
2024