Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions
This research addresses the problem of trust in AI decision support for users, providing insights into human-AI interaction dynamics, though it is incremental in building on existing behavioral studies.
The study investigated whether humans trust AI advice more than human advice by recruiting over 1100 participants across experimental settings, finding that trust depends on beliefs about performance rather than the source, and proposing a two-stage model to explain these interactions.
In decision support applications of AI, the AI algorithm's output is framed as a suggestion to a human user. The user may ignore this advice or take it into consideration to modify their decision. With the increasing prevalence of such human-AI interactions, it is important to understand how users react to AI advice. In this paper, we recruited over 1100 crowdworkers to characterize how humans use AI suggestions relative to equivalent suggestions from a group of peer humans across several experimental settings. We find that participants' beliefs about how human versus AI performance on a given task affects whether they heed the advice. When participants do heed the advice, they use it similarly for human and AI suggestions. Based on these results, we propose a two-stage, "activation-integration" model for human behavior and use it to characterize the factors that affect human-AI interactions.