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

Elena Sergeeva, Anastasia Sergeeva, Huiyun Tang, Kerstin Bongard-Blanchy, Peter Szolovits
10/22/2023
cs.CLcs.AIcs.HC

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Abstract

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.

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Cite this paper

@article{sergeeva2023right,
  title  = {Right, No Matter Why: AI Fact-checking and AI Authority in Health-related Inquiry Settings},
  author = {Elena Sergeeva and Anastasia Sergeeva and Huiyun Tang and Kerstin Bongard-Blanchy and Peter Szolovits},
  year   = {2023},
  eprint = {2310.14358},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.14358v1}
}

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