{
  "id": 13812,
  "url": "https://arxiv.org/abs/2407.14766",
  "title": "Fairness Interventions in Classification: A Study on AI Explainability",
  "summary": "arXiv:2407.14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds. Our main argument is that even as a gap in Demographic Parity is used to diagnose inequality between groups, Equalized Odds constitutes a more reliable fairness crit",
  "authors": "Thomas Souverain, Paul \\'Egr\\'e",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T04:00:00.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13812",
  "original_url": "https://arxiv.org/abs/2407.14766",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}