A Taxonomy of Challenges to Curating Fair Datasets

Dora Zhao, Morgan Klaus Scheuerman, Pooja Chitre, Jerone T. A. Andrews, Georgia Panagiotidou +3 more
6/10/2024
cs.LGcs.CY

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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.

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Cite this paper

@article{zhao2024taxonomy,
  title  = {A Taxonomy of Challenges to Curating Fair Datasets},
  author = {Dora Zhao and Morgan Klaus Scheuerman and Pooja Chitre and Jerone T. A. Andrews and Georgia Panagiotidou and Shawn Walker and Kathleen H. Pine and Alice Xiang},
  year   = {2024},
  eprint = {2406.06407},
  archivePrefix = {arXiv},
  doi    = {10.48550/arXiv.2406.06407},
  url    = {http://arxiv.org/abs/2406.06407v2},
  journal = {NEURIPS 2024 2024}
}

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