Algorithm Aversion is Motivated: Symmetrical Partisan Bias in the Acceptance of Algorithmic Policy Advisors
(A2025-125323)

AUTHORS

Yann Cornil, University of British Columbia; Marius Claudy, University College Dublin; Susan Calderon Urbina, University College Dublin

ABSTRACT

We examine the influence of political orientation on attitudes toward Artificial Intelligence in policy-making. Liberals (vs. conservatives) are not necessarily more or less favourable to AI: They assess the desirability of AI based on whether it could disrupt a political status quo that aligns—or conflicts—with their beliefs. Specifically, liberals show higher acceptance of AI in policy-making, and also find AI more impartial, when the policy in question is conservative- (vs liberal-) leaning. Symmetrically, conservatives show higher acceptance of AI, and find it more impartial, when the policy is liberal- (vs. conservative-) leaning. These findings seem specific to AI, and do not generalize to any other policy advising entities. We contribute to past research by explaining past inconsistent findings on political orientation and algorithm aversion based on motivated reasoning, and by providing further evidence for the “symmetrical partisan bias” hypothesis in political psychology.