Paper Number
ICIS2025-1748
Paper Type
Short
Abstract
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.
Recommended Citation
Gao, Grace Yuekun; Benbasat, Izak; Lyu, Jueni; and Cheung, Christy M.K., "When Anthropomorphism Meets Explainability: Mitigating Job Applicants’ Algorithm Aversion Toward AI Recruiters" (2025). ICIS 2025 Proceedings. 8.
https://aisel.aisnet.org/icis2025/is_good/is_good/8
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.
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06-SocialGood