Is There Fairness in AI?
Abstract
As predictive artificial intelligence (AI) technologies increasingly steer workplace decisions, debates around fairness have intensified. Existing research often approaches fairness either as a set of universal principles supported or undermined by algorithms, or as a product of social interpretations, thereby providing either technologically deterministic or purely social accounts. Drawing on an ethnographic study of a human resources (HR) department of a large international company that introduced AI in hiring, this study offers an alternative view that shifts focus to how fairness emerges through the ways people define, embed, and perform values with algorithms. Taking a sociomaterial perspective, we find that the introduction and use of AI resulted in crowding out expert practices of performing fairness, favouring instead the version performed by HR. Our process model explains this outcome by the growing symbiosis between HR's professional mandate for fairness and AI procedures, where each legitimizes, shapes, and protects the other over time. This study thus shows that fairness is not pre‐given but constantly redefined and enacted through evolving associations between professional mandates and AI technologies.
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The result of the system was narrower pools and dissatisfied hiring managers.
“The result was narrower pools and dissatisfied hiring managers.”
From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [35]
A three-year field study at a consumer goods firm processing more than 10,000 applicants annually found the system increased consistency.
“A three-year field study at a consumer goods firm processing more than 10,000 applicants annually found a subtler version.The system increased consistency.”
From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [35]
The system hard-coded one definition of fairness--standardization under uniform rules--and progressively deprioritized managers' local judgment about role requirements, labor markets, and team needs.
“However, it hard-coded one definition of fairness--standardization under uniform rules--and progressively deprioritized managers' local judgment about role requirements, labor markets, and team needs [35].”
From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [35]
Fairness was not embedded in the code; it was negotiated by the people who built and deployed the system, and then frozen.
“Fairness was not embedded in the code.It was negotiated by the people who built and deployed the system, and then frozen.”
From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [35]
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Cited by
Sorted by citations
Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out
Ben Oz, R., Granot, N., Tzabbar, D., & Tzafrir, S. (2026) · doi:10.2139/ssrn.7489654
- Authors
- van den Broek, E., Sergeeva, A., & Huysman, M.
- Affiliations
- Stockholm School of Economics
- Vrije Universiteit Amsterdam
- Venue
- Journal of Management Studies
- Publisher
- Wiley
- Published
- 2025-08-24
- Volume
- 63
- Issue
- 6
- Pages
- 3494-3531
- ISSN
- 0022-2380
- 1467-6486
- Cited by
- 17
- References
- 10
- Language
- en
- Funders
- Marcus och Amalia Wallenbergs minnesfond (2021.0133)
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