{
  "id": 3074,
  "url": "https://arxiv.org/abs/2606.31704v1",
  "title": "WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation",
  "summary": "The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups. A key obstacle to studying and mitigating such biases is the lack of face detection datasets with sensitive feature annotations. To address this gap, we introduce WIDER-FAIR, a new dataset built on the widely used WIDER-FACE benchmark, manually annotated with the perceived ethnicity and sex of each face. The datase",
  "authors": "Maxime Moussi, Benoît Ronval, Siegfried Nijssen, Félicien Schiltz",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-30T14:10:42.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3074",
  "original_url": "https://arxiv.org/abs/2606.31704v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}