</Click to begin your digital interview>: Applicants' experiences with discrimination explain their reactions to algorithms in personnel selection.
Published In: International Journal of Selection & Assessment, 2023, v. 31, n. 2. P. 252 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Koch‐Bayram, Irmela F.; Kaibel, Chris; Biemann, Torsten; Triana, María del Carmen 3 of 3
Abstract
Algorithms might prevent prejudices and increase objectivity in personnel selection decisions, but they have also been accused of being biased. We question whether algorithm‐based decision‐making or providing justifying information about the decision‐maker (here: to prevent biases and prejudices and to make more objective decisions) helps organizations to attract a diverse workforce. In two experimental studies in which participants go through a digital interview, we find support for the overall negative effects of algorithms on fairness perceptions and organizational attractiveness. However, applicants with discrimination experiences tend to view algorithm‐based decisions more positively than applicants without such experiences. We do not find evidence that providing justifying information affects applicants—regardless of whether they have experienced discrimination or not. Practitioner points: Algorithms evaluating digital interviews violate applicants' fairness perceptions and diminish organizational attractivenessApplicants with discrimination experiences tend to view algorithm‐based decisions more positively Information about the use of algorithms in hiring could be detrimentalThe use of algorithms could be an alternative to hiring prior victims of discrimination [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Selection & Assessment. 2023/06, Vol. 31, Issue 2, p252
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2023
- ISSN:0965-075X
- DOI:10.1111/ijsa.12417
- Accession Number:163911594
- Copyright Statement:Copyright of International Journal of Selection & Assessment is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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