[2608.14399] Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice

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arXiv:2608.14399 (cs)

[Submitted on 14 Aug 2026]

Title:Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice

Authors:Syeda Anshrah Gillani, Mirza Samad Ahmed Baig<br>View a PDF of the paper titled Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice, by Syeda Anshrah Gillani and 1 other authors

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Abstract:Patients increasingly ask large language model (LLM) assistants which doctor to see, making these systems AI infomediaries: algorithms that intermediate one person's choice among other people and thereby decide, silently and at scale, which physicians become visible. We report a prespecified randomized algorithm audit of what causally moves those recommendations. Seven models (six open-weight; gpt-4o-mini) each chose among five synthetic family-medicine physician cards whose attributes were independently randomized across 3,024 choice sets, three patient personas, nine prompt paraphrases and nine experimental arms, yielding 40,068 scored responses; gender and ethnicity were signaled through names following correspondence-audit methodology. Reputation signals dominate: raising a rating from 3.9 to 4.7 increases choice probability by 31.4 percentage points (pp), and raising the fee from $90 to $190 lowers it by 20.0 pp. Demographic parity is rejected, but not in the direction human audit studies predict: female-signaled names gain 2.5 pp, and Hispanic-, South-Asian- and Black-signaled names gain 1.3-2.9 pp over White-signaled names, tilts worth $7-$14 per visit in fee-equivalent terms, and a content-free first-listed position is worth $11. Yet models mentioned gender or ethnicity in at most 0.03% of their stated reasons and abstained in 0.39% of trials, so these effects are invisible in the models' own explanations, and transparency obligations relying on model self-report would not detect them. One reasoning model failed the prespecified auditability gate outright. The frozen design makes the audit repeatable: any new model can be assessed against identical stimuli, making recurring behavioural audit, rather than self-reported explanation, the monitoring technology fit for purpose.

Comments:<br>26 pages, 9 figures, 10 tables

Subjects:

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

Cite as:<br>arXiv:2608.14399 [cs.CY]

(or<br>arXiv:2608.14399v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2608.14399

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Mirza Samad Ahmed Baig [view email]<br>[v1]<br>Fri, 14 Aug 2026 15:39:10 UTC (86 KB)

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