{
  "id": 4555,
  "url": "https://arxiv.org/abs/2605.10521v1",
  "title": "DuetFair: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Segmentation",
  "summary": "Medical image segmentation models can perform unevenly across subgroups. Most existing fairness methods focus on improving average subgroup performance, implicitly treating each subgroup as internally homogeneous. However, this can hide difficult cases within a subgroup, where high-loss samples are obscured by the subgroup mean. We call this problem \\textbf{intra-group hidden failure}. To solve this, we propose \\textbf{DuetFair} mechanism, a dual-axis fairness framework that jointly considers in",
  "authors": "Yiqi Tian, Sangjoon Park, Bo Zeng, Pengfei Jin, Yujin Oh, Quanzheng Li",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T13:08:35.000Z",
  "fetched_at": "2026-07-14T16:31:03.581Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4555",
  "original_url": "https://arxiv.org/abs/2605.10521v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}