{
  "id": 15250,
  "url": "https://arxiv.org/abs/2607.28608v1",
  "title": "KAISEN: Reproducible Subgroup Fairness Auditing for Clinical Risk Models",
  "summary": "Clinical risk models routinely achieve strong aggregate performance while producing materially different error rates across patient subgroups. Audit pipelines have been proposed to catch this, but their components are rarely stress-tested, so it is unclear which parts of an audit can be trusted and under what conditions. We present KAISEN, a five-phase audit pipeline covering subgroup stratification, disparity measurement, mechanism diagnostics, post-hoc mitigation, and drift monitoring, evaluat",
  "authors": "Sparsh Roy, Samuel Girmachew, Nishita Chavan",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T17:57:18.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/15250",
  "original_url": "https://arxiv.org/abs/2607.28608v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}