AI as moral cover: How algorithmic bias exploits psychological mechanisms to perpetuate social inequality

Abstract

Algorithmic decision-making systems are increasingly shaping critical social outcomes (e.g., hiring, lending, criminal justice, healthcare), yet technical approaches to bias mitigation ignore crucial psychological mechanisms that enable discriminatory use. To address this gap, I integrate motivated reasoning, system justification, and moral disengagement theories to argue that AI systems may function as “moral cover,” allowing users to perpetuate inequality while maintaining beliefs in their own objectivity. Users often demonstrate “selective adherence,” following algorithmic advice when it confirms stereotypes while dismissing counter-stereotypical outputs. System justification motives lead people to defend discriminatory algorithmic outcomes as legitimate, “data-driven” decisions. Moral disengagement mechanisms (including responsibility displacement, euphemistic labeling, and advantageous comparison) can enable discrimination while preserving moral self-regard. Finally, I argue that understanding AI bias as fundamentally psychological rather than merely technical demands interventions addressing these underlying psychological processes alongside algorithmic improvements.