{
  "id": 7191,
  "url": "https://arxiv.org/abs/2603.14947v1",
  "title": "FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data",
  "summary": "Machine learning models deployed in critical care settings exhibit demographic biases, particularly gender disparities, that undermine clinical trust and equitable treatment. This paper introduces FairMed-XGB, a novel framework that systematically detects and mitigates gender-based prediction bias while preserving model performance and transparency. The framework integrates a fairness-aware loss function combining Statistical Parity Difference, Theil Index, and Wasserstein Distance, jointly opti",
  "authors": "Mitul Goswami, Romit Chatterjee, Arif Ahmed Sekh",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-16T07:57:40.000Z",
  "fetched_at": "2026-07-14T16:33:03.572Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7191",
  "original_url": "https://arxiv.org/abs/2603.14947v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}