{
  "id": 4952,
  "url": "https://arxiv.org/abs/2605.03998v1",
  "title": "EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage",
  "summary": "Emergency department triage assigns patients an acuity score that determines treatment priority, and clinical evidence documents persistent gender disparities in human acuity assessment. As hospitals pilot large language models (LLMs) as triage decision support, a critical question is whether these models reproduce or mitigate known biases. We present EQUITRIAGE, a fairness audit of LLM-based ESI assignment evaluating five models (Gemini-3-Flash, Nemotron-3-Super, DeepSeek-V3.1, Mistral-Small-3.",
  "authors": "Richard J. Young, Alice M. Matthews",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": "google,mistral,deepseek",
  "regions": null,
  "published_at": "2026-05-05T17:20:55.000Z",
  "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/4952",
  "original_url": "https://arxiv.org/abs/2605.03998v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}