{
  "id": 6229,
  "url": "https://arxiv.org/abs/2604.16450v2",
  "title": "Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program",
  "summary": "Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently. FairLogue, a toolkit for intersectional fairness auditing, was applied across multiple clinical prediction tasks to evaluate disparities across combined demographic groups. Using the All of Us dataset, two published models were selected for replication and evaluation: (A) prediction of selective serotonin reuptak",
  "authors": "Nick Souligne, Vignesh Subbian",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-07T19:50:10.000Z",
  "fetched_at": "2026-07-14T16:32:20.056Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6229",
  "original_url": "https://arxiv.org/abs/2604.16450v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}