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.
@article{arxiv.2107.07015,
title = {Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions},
author = {Kailas Vodrahalli and Roxana Daneshjou and Tobias Gerstenberg and James Zou},
journal= {arXiv preprint arXiv:2107.07015},
year = {2022}
}
Comments
Conference on Artificial Intelligence, Ethics, and Society (AIES 2022)