{"slug": "whose-doctor-does-the-ai-recommend-an-algorithm-audit-of-llms-in-physician", "title": "Whose doctor does the AI recommend? An algorithm audit of LLMs in physician", "summary": "A prespecified randomized algorithm audit of seven large language models (six open-weight and gpt-4o-mini) found that reputation signals dominate physician recommendations, with raising a rating from 3.9 to 4.7 increasing choice probability by 31.4 percentage points and raising the fee from $90 to $190 lowering it by 20.0 percentage points. The audit, which analyzed 40,068 scored responses across 3,024 choice sets, also found demographic parity is rejected in the opposite direction of human audit studies: 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, yet models mentioned gender or ethnicity in at most 0.03% of stated reasons, making these effects invisible to self-report. One reasoning model failed the prespecified auditability gate, and the authors argue recurring behavioral audit is the monitoring technology fit for purpose.", "body_md": "# Computer Science > Computers and Society\n\n[Submitted on 14 Aug 2026]\n\n# Title:Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice\n\n[View PDF](/pdf/2608.14399)\n\n[HTML (experimental)](https://arxiv.org/html/2608.14399v1)\n\nAbstract: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.\n\n## Submission history\n\nFrom: Mirza Samad Ahmed Baig [[view email](/show-email/57a95a53/2608.14399)]\n\n**[v1]** Fri, 14 Aug 2026 15:39:10 UTC (86 KB)\n\n### Current browse context:\n\ncs.CY\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/whose-doctor-does-the-ai-recommend-an-algorithm-audit-of-llms-in-physician", "canonical_source": "https://arxiv.org/abs/2608.14399", "published_at": "2026-08-18 00:07:06+00:00", "updated_at": "2026-08-18 00:41:07.711346+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-ethics", "ai-policy"], "entities": ["gpt-4o-mini", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/whose-doctor-does-the-ai-recommend-an-algorithm-audit-of-llms-in-physician", "markdown": "https://wpnews.pro/news/whose-doctor-does-the-ai-recommend-an-algorithm-audit-of-llms-in-physician.md", "text": "https://wpnews.pro/news/whose-doctor-does-the-ai-recommend-an-algorithm-audit-of-llms-in-physician.txt", "jsonld": "https://wpnews.pro/news/whose-doctor-does-the-ai-recommend-an-algorithm-audit-of-llms-in-physician.jsonld"}}