{
  "id": 4945,
  "url": "https://arxiv.org/abs/2605.08198v1",
  "title": "FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings",
  "summary": "We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applications, with particular focus on low-resource and low-income country (LMIC) settings such as Bangladesh. FairHealth addresses four critical gaps in existing healthcare AI toolkits: (1) the absence of integrated fairness auditing for biosignals and clinical tabular data; (2) the lack of privacy-preserving federated learning tools compatible with stan",
  "authors": "Farjana Yesmin",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-05T20:55:39.000Z",
  "fetched_at": "2026-07-14T16:31:21.935Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4945",
  "original_url": "https://arxiv.org/abs/2605.08198v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}