Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Gonzalez, M. F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe, C. E., Justenhoven, R., & Martin, N. R. Computers in Human Behavior, 130:107179, 2022. ![Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex While many organizations' hiring practices now incorporate artificial intelligence (AI) and machine learning (ML), research suggests that job applicants may react negatively toward AI/ML-based selection practices. In the current research, we thus examined how organizations might mitigate adverse reactions toward AI/ML-based selection processes. In two between-subjects experiments, we recruited online samples of participants (undergraduate students and Prolific panelists, respectively) and presented them with vignettes representing various selection systems and measured participants' reactions to them. In Study 1, we manipulated (a) whether the system was managed by a human decision-maker, by AI/ML, or a combination of both (an “augmented” approach), and (b) the selection stage (screening, final stage). Results indicated that participants generally reacted more favorably toward augmented and human-based approaches, relative to AI/ML-based approaches, and further depended on participants' pre-existing familiarity levels with AI. In Study 2, we sought to replicate our findings within a specific process (selecting hotel managers) and application method (handling interview recordings). We found again that reactions toward the augmented approach generally depended on participants’ familiarity levels with AI. Our findings have implications for how (and for whom) organizations should implement AI/ML-based practices.
@article{GONZALEZ2022107179,
title = {Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes},
journal = {Computers in Human Behavior},
volume = {130},
pages = {107179},
year = {2022},
issn = {0747-5632},
doi = {https://doi.org/10.1016/j.chb.2022.107179},
url = {https://www.sciencedirect.com/science/article/pii/S0747563222000012},
author = {Manuel F. Gonzalez and Weiwei Liu and Lei Shirase and David L. Tomczak and Carmen E. Lobbe and Richard Justenhoven and Nicholas R. Martin},
keywords = {Applicant reactions, Artificial intelligence, Machine learning, Employee selection, Augmented approach, Familiarity},
abstract = {While many organizations' hiring practices now incorporate artificial intelligence (AI) and machine learning (ML), research suggests that job applicants may react negatively toward AI/ML-based selection practices. In the current research, we thus examined how organizations might mitigate adverse reactions toward AI/ML-based selection processes. In two between-subjects experiments, we recruited online samples of participants (undergraduate students and Prolific panelists, respectively) and presented them with vignettes representing various selection systems and measured participants' reactions to them. In Study 1, we manipulated (a) whether the system was managed by a human decision-maker, by AI/ML, or a combination of both (an “augmented” approach), and (b) the selection stage (screening, final stage). Results indicated that participants generally reacted more favorably toward augmented and human-based approaches, relative to AI/ML-based approaches, and further depended on participants' pre-existing familiarity levels with AI. In Study 2, we sought to replicate our findings within a specific process (selecting hotel managers) and application method (handling interview recordings). We found again that reactions toward the augmented approach generally depended on participants’ familiarity levels with AI. Our findings have implications for how (and for whom) organizations should implement AI/ML-based practices.}
}
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