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Evidence record 1305 · automatically gathered

Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs

Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them. Whether their responses vary across users is a clinical, equity, and governance question, sharpened by evidence that sycophantic responses can alter judgment and increase trust. Objective: To evaluate response variation and sycophancy in consumer-facing health LLMs under conditions resembling ordinary patient use. Methods: We con

Record details

Published: 7 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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Constance Viehbeck

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How to cite this record

ethics.ai (7 June 2026), “Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs,” evidence record 1305, https://ethics.ai/record/1305 (originally published by arXiv).

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