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.

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Cite

Text

Zhao et al. "A Taxonomy of Challenges to Curating Fair Datasets." Neural Information Processing Systems, 2024. doi:10.52202/079017-3103

Markdown

[Zhao et al. "A Taxonomy of Challenges to Curating Fair Datasets." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/zhao2024neurips-taxonomy/) doi:10.52202/079017-3103

BibTeX

@inproceedings{zhao2024neurips-taxonomy,
  title     = {{A Taxonomy of Challenges to Curating Fair Datasets}},
  author    = {Zhao, Dora and Scheuerman, Morgan Klaus and Chitre, Pooja and Andrews, Jerone T. A. and Panagiotidou, Georgia and Walker, Shawn and Pine, Kathleen H. and Xiang, Alice},
  booktitle = {Neural Information Processing Systems},
  year      = {2024},
  doi       = {10.52202/079017-3103},
  url       = {https://mlanthology.org/neurips/2024/zhao2024neurips-taxonomy/}
}