Beyond Mere Algorithm Aversion: Are Judgments About Computer Agents More Malleable?

Several studies have reported algorithm aversion, reflected in harsher judgments about computers that commit errors, compared to humans who commit the same errors. Two online studies (N = 67, N = 252) tested whether similar effects can be obtained with a referential communication task. Participants were tasked with identifying Japanese kanji characters based on written descriptions allegedly coming from a human or an AI source. Crucially, descriptions were either flawed (ambiguous) or not. Both concurrent measures during experimental trials and pre-post questionnaire data about the source were captured. Study 1 revealed patterns of algorithm aversion, but also pointed at an opposite effect of “algorithm benefit”: ambiguous descriptions by an AI (vs. human) were evaluated more negatively, but non-ambiguous descriptions were evaluated more positively, suggesting the possibility that judgments about AI sources are more malleable. Study 2 tested this prediction. While human and AI sources did not differ regarding concurrent measures, questionnaire data revealed several patterns that are consistent with the malleability explanation.