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Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions
Kailas Vodrahalli · James Zou

@
in Workshop on Socially Responsible Machine Learning »

In many applications of AI, the algorithm’s output is framed as a suggestion to a human user. The user may ignore the advice or take it into con- sideration to modify his/her decisions. With the increasing prevalence of such human-AI interac- tions, it is important to understand how users act (or do not act) upon AI advice, and how users re- gard advice differently if they believe the advice come from an “AI” versus other human. In this paper, we characterize how humans use AI sug- gestions relative to equivalent suggestions from a group of peer humans across several experimental settings. We find that participants’ beliefs about the human versus AI performance on a given task affects whether or not they heed the advice. When participants decide the use the advice, they do so similarly for human and AI suggestions. These results provide insights into factors that affect human-AI interactions.

Author Information

Kailas Vodrahalli (Stanford University)
James Zou (Stanford University)

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