Ethical Considerations for Responsible Data Curation
This work addresses ethical data curation for researchers and practitioners in computer vision, but it appears incremental as it builds on existing practices and guidelines.
The paper tackles the problem of privacy and bias in human-centric computer vision data curation by proposing proactive, domain-specific recommendations to address these issues, though no concrete results or numbers are provided.
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.