How to improve fairness perceptions of AI in hiring: The crucial role of positioning and sensitization
Open accessAbstract
Companies increasingly deploy artificial intelligence (AI) technologies in their personnel recruiting and selection processes to streamline them, thus making them more efficient, consistent, and less human biased (Chamorro-Premuzic, Polli, & Dattner, 2019) . However, prior research found that applicants prefer face-to-face interviews compared with AI interviews, perceiving them as less fair (e.g., Acikgoz, Davison, Compagnone, & Laske, 2020) . Additionally, emerging evidence exists that contextual influences, such as the type of task for which AI is used (Lee, 2018) , or applicants’ individual differences (Langer, König, Sanchez, & Samadi, 2019) , may influence applicants’ reactions to AI-powered selection. The purpose of our study was to investigate whether adjusting process design factors may help to improve people's fairness perceptions of AI interviews. The results of our 2 x 2 x 2 online study (N = 404) showed that the positioning of the AI interview in the overall selection process, as well as participants’ sensitization to its potential to reduce human bias in the selection process have a significant effect on people’s perceptions of fairness. Additionally, these two process design factors had an indirect effect on overall organizational attractiveness mediated through applicants’ fairness perceptions. The findings may help organizations to optimize their deployment of AI in selection processes to improve people’s perceptions of fairness and thus attract top talent.
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Utilizing AI fairness techniques rectifies model biases in automated hiring, fostering impartial hiring decisions independent of attributes unrelated to job proficiency.
“Utilizing AI fairness techniques rectifies these model biases, fostering impartial hiring decisions independent of attributes unrelated to job proficiency [309].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [309]
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References 37 · Cited by 18
Research works printed but not linked: 3
- Ployhart, R. E., Weekley, J. A., Holtz, B. C., & Kemp, C. (2003). Web-based and paper-and-pencil testing of applicants in a proctored setting: Are personality, biodata, and situational judgement tests comparable? Personnel Psychology, 56, 733-752. AI Ethics Journal 31. Ross, J., Irani, L., Silberman, M. S., Zaldivar, A., & Tomlinson, B. (2010). Who are the crowdworkers? Shifting demographics in Mechanical Turk. In 28th Annual CHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA.
- Ryan, A. M., & Ployhart, R. E. (2000). Applicant perceptions of selection procedures and decisions: A critical review and agenda for the future. Journal of Management, 26, 565-606.
- Warszta, T. (2012). Application of Gilliland's model of applicants' reactions to the field of web-based selection (unpublished dissertation). Christian-Albrechts Universität Kiel, Kiel.
- Authors
- Hunkenschroer, A.
- Affiliations
- Technical University of Munich (DE)
- Corresponding author
- Hunkenschroer, Anna
- ORCID
- Hunkenschroer, Anna
- Venue
- AI Ethics Journal
- Publisher
- AI Robotics Ethics Society
- Published
- 2021-07-16
- Volume
- 2
- Issue
- 2
- ISSN
- 2690-1625 (ISSN-L)
- 2690-1633
- OpenAlex
- W3185449909
- Cited by
- 18
- References
- 37
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 2 · 2021: 1
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- AI Ethics Journal (publishedVersion)
- Funders
- Technische Universität München
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
- Keywords
- artificial intelligence in recruitment, perceived fairness, applicant perceptions, process design, sensitization, employer attractiveness, personnel selection, face-to-face interviews, candidate recruitment
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