Practical recommendations for responsibly curating human-centric computer vision datasets for fairness and robustness evaluations, addressing privacy and bias concerns.
NeurIPS 2023 Datasets and Benchmarks (Oral)
Jerone Andrews, Dora Zhao, William Thong, Apostolos Modas, Orestis Papakyriakopoulos, Alice Xiang
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If you use any part of this work, please give appropriate credit by using the following BibTeX entry:
@inproceedings{
andrews2023ethical,
title={Ethical Considerations for Responsible Data Curation},
author={Jerone Andrews and Dora Zhao and William Thong and Apostolos Modas and Orestis Papakyriakopoulos and Alice Xiang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=Qf8uzIT1OK}
}
To guide curators towards more ethical yet resource-intensive curation, we also provide a responsible data curation checklist for fairness and robustness evaluations.
The checklist is provided in four formats: [.tex],
[.txt], [.docx],
and [.md].
The checklist is made available under a Creative Commons Attribution-Share Alike 4.0 International License.