mlsanityData to 7 Oct 2026

Right, No Matter Why: AI Fact-checking and AI Authority in Health-related Inquiry Settings

arXiv 2310.14358 ↗First posted 22 Oct 2023cs.CL cs.AI cs.HCcited by 0

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Abstract (arXiv)

Previous research on expert advice-taking shows that humans exhibit two contradictory behaviors: on the one hand, people tend to overvalue their own opinions undervaluing the expert opinion, and on the other, people often defer to other people's advice even if the advice itself is rather obviously wrong. In our study, we conduct an exploratory evaluation of users' AI-advice accepting behavior when evaluating the truthfulness of a health-related statement in different "advice quality" settings. We find that even feedback that is confined to just stating that "the AI thinks that the statement is false/true" results in more than half of people moving their statement veracity assessment towards the AI suggestion. The different types of advice given influence the acceptance rates, but the sheer effect of getting a suggestion is often bigger than the suggestion-type effect.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 57 references that are in the map. First posted on arXiv 22 Oct 2023.

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References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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3 more
  1. 2023Do We Still Need Clinical Language Models?cited by 97
  2. 2021Principles of Explanation in Human-AI Systemscited by 25
  3. 2020Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QAcited by 17

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