  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.
