Do Humans Trust Advice More if it Comes from AI?

Kailas Vodrahalli · Roxana Daneshjou · Tobias Gerstenberg · James Zou

63 citations0/0 files storedProceedings of the AAAI/ACM Conference on AI Ethics and Society

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Human-Automation Interaction and Safety Ethics and Social Impacts of AI Social Robot Interaction and HRI

Abstract

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.

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References

2018Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management 2002The Perceived Utility of Human and Automated Aids in a Visual Detection Task 2009The relative influence of advice from human experts and statistical methods on forecast adjustments 2017Understanding algorithm aversion: When is advice from automation discounted?2006Effects of task difficulty on use of advice 1997Precision and accuracy of judgmental estimation 2007Effects of Information Source, Pedigree, and Reliability on Operator Interaction With Decision Support Systems 2013Cheap talk and credibility: The consequences of confidence and accuracy on advisor credibility and persuasiveness

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