Right, No Matter Why: AI Fact-checking and AI Authority in Health-related Inquiry Settings
This addresses the problem of AI authority in health fact-checking for users, though it is incremental as it builds on prior research on expert advice-taking.
The study investigated how users adjust their assessment of health-related statement truthfulness based on AI advice, finding that over half of participants shifted their assessment towards the AI suggestion even with minimal feedback.
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.