{
  "id": 4307,
  "url": "https://arxiv.org/abs/2605.15312v1",
  "title": "Beyond Performance Disparities: A Three-Level Audit of Representational Harm in CelebA",
  "summary": "Large-scale facial datasets like CelebA are widely used in computer vision, yet the cultural biases embedded in their labels remain underexplored. Fairness research has distinguished representational from allocational harms, but audits of computer vision datasets have mostly examined categorical labels, leaving open how such harms appear in learned features and model attention. This paper examines CelebA at three levels: dataset structure, learned feature weights, and spatial attention, focusing",
  "authors": "Sieun Park, Yuanmo He",
  "category": "research",
  "topics": "bias-fairness,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-14T18:25:17.000Z",
  "fetched_at": "2026-07-14T16:30:54.920Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4307",
  "original_url": "https://arxiv.org/abs/2605.15312v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}