Research

This paper investigates the source of algorithmic aversion, defined as the unwillingness to accept advice or decisions made by algorithms. Algorithmic aversion is conceptualized as a form of individual partiality, driven either by belief-based factors attributed to differences in perceived ability, or preference-based factors which reflect a disamenity associated with selecting algorithms. To empirically test the predictions of the model, a preregistered online experiment was conducted, where participants evaluated answers to objective and subjective economic questions, with varying information of whether the source was human or algorithm. The results provide no evidence of algorithmic aversion in the evaluation task: participants did not systematically favor human-generated answers over algorithm-generated ones. These results imply that algorithmic aversion may not be as robust or uniform as previously assumed.