Ethical Considerations for Responsible Data Curation
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Abstract (arXiv)
Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 125 of its 377 references that are in the map. First posted on arXiv 7 Feb 2023.
Builds on
125 of 377 references.
References · 125
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