{
  "id": 184,
  "url": "https://arxiv.org/abs/2607.05574v1",
  "title": "Whose fairness? Structural concentration in AI bias research",
  "summary": "Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias. Yet the fairness definitions, benchmarks, and debiasing frameworks on which this methodology rests are treated as universal while being produced by a research community whose composition has never been characterized. We show that the AI bias research are structurally concentrated, and that this ",
  "authors": "Abhash Shrestha, Subigya Gautam, Anu Sapkota, Sanju Tiwari, Tek Raj Chhetri",
  "category": "research",
  "topics": "bias-fairness,regulation,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-06T19:17:53.000Z",
  "fetched_at": "2026-07-14T14:14:19.971Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/184",
  "original_url": "https://arxiv.org/abs/2607.05574v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}