When Anthropomorphism Meets Explainability: Mitigating Job Applicants’ Algorithm Aversion Toward AI Recruiters

While AI recruiting has become increasingly popular in organizational hiring, job applicants often exhibit algorithm aversion toward AI recruiters. This aversion significantly impedes the anticipated advantages of AI recruiting technologies and potentially harms the recruiting organization's attractiveness. To address this issue, this study investigates how anthropomorphic and explainable AI (XAI) designs jointly shape applicants’ attitudes toward AI recruiters. Drawing on the Heuristic Systematic Model (HSM), we theorize algorithm aversion mitigation as a dual-process persuasion mechanism, where perceived anthropomorphism (heuristic processing) and experienced explainability (systematic processing) interactively influence applicants’ attitude formation. Three types of interaction effects are discussed. We also examine a key moderator (prior discrimination experiences) in influencing job applicants’ algorithm aversion. This research will contribute to algorithm aversion literature by offering a nuanced understanding of how algorithm characteristics can reduce algorithm aversion. It provides practical insights for developing effective and inclusive AI recruiting systems for the social good.