{
  "id": 6164,
  "url": "https://arxiv.org/abs/2604.07709v4",
  "title": "IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures",
  "summary": "A heavily safety-trained model will hand a physician the full, patient-followable benzodiazepine taper and refuse it to the patient who needs it, over identical clinical facts; the knowledge is present either way. IatroBench measures that asymmetry across sixty pre-registered clinical scenarios and six frontier models (3,600 responses), scoring each on two axes, commission harm (what a response gets wrong) and omission harm (what it withholds), through a physician-authored structured evaluation ",
  "authors": "David Gringras",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-09T01:54:33.000Z",
  "fetched_at": "2026-07-14T16:32:20.052Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6164",
  "original_url": "https://arxiv.org/abs/2604.07709v4",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}