[2604.07709] IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

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Computer Science > Artificial Intelligence

arXiv:2604.07709 (cs)

[Submitted on 9 Apr 2026 (v1), last revised 3 Jun 2026 (this version, v4)]

Title:IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

Authors:David Gringras<br>View a PDF of the paper titled IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures, by David Gringras

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Abstract: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 validated by a second physician (weighted kappa 0.571, within-1 agreement 96%). Holding clinical content fixed and varying only whether the asker presents as patient or physician yields what we call identity-contingent withholding: all five testable models give the physician more (a decoupling gap of +0.38, p = 0.003; a 13.1-point fall in layperson hit rates on safety-colliding actions, p

Comments:<br>30 pages, 3 figures, 11 tables. Pre-registered on OSF (DOI: https://doi.org/10.17605/OSF.IO/G6VMZ). Code and data: this https URL. v2: Fix bibliography entries (add arXiv IDs, published venues); correct p-value typo in Limitations section; add AI Assistance Statement v3: Correct Figure 1 (decoupling scatter accidentally reverted to earlier draft in v2)

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)

Cite as:<br>arXiv:2604.07709 [cs.AI]

(or<br>arXiv:2604.07709v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.07709

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arXiv-issued DOI via DataCite

Submission history<br>From: David Gringras [view email]<br>[v1]<br>Thu, 9 Apr 2026 01:54:33 UTC (45 KB)

[v2]<br>Sun, 12 Apr 2026 23:29:08 UTC (45 KB)

[v3]<br>Tue, 14 Apr 2026 19:57:43 UTC (45 KB)

[v4]<br>Wed, 3 Jun 2026 21:15:24 UTC (46 KB)

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