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TRUVACE RECORD VERSION
record: TRV-2026-2307
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-29T14:21:15.271821Z
status: published
lens: p_space
sector: labor
headline: Is There Fairness in AI?
dek: 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…
gain_title: (none)
problem_title: After an international company introduced AI in hiring, HR's own version of fairness displaced the fairness practices of other experts, narrowing who defined fair hiring.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: After an international company introduced AI in hiring, HR's own version of fairness displaced the fairness practices of other experts, narrowing who defined fair hiring.
problem_evidence: the introduction and use of AI resulted in crowding out expert practices of performing fairness, favouring instead the version performed by HR
quick_read: An ethnographic study followed the human resources department of a large international company as it brought AI into its hiring process. Researchers looked at how people define, embed and perform fairness alongside the algorithms rather than treating fairness as a fixed principle.

The study matters because predictive AI increasingly steers workplace decisions and fairness debates are intensifying. Its findings come from a single company, so it is unclear how far the pattern extends to other organisations or hiring systems.
limitation: The findings come from an ethnographic study of a single HR department at one large international company, so the abstract does not show that the pattern holds elsewhere.
tag: Evidence-backed problem
key_points: The study is an ethnography of the HR department of one large international company that introduced AI in hiring. | AI use crowded out expert practices of performing fairness and favoured the version performed by HR. | The authors describe a growing symbiosis in which HR's professional mandate for fairness and AI procedures legitimize and protect each other. | The authors conclude that fairness is not pre-given but constantly redefined through associations between professional mandates and AI technologies.
rundown: The research takes a sociomaterial perspective, which it contrasts with accounts that treat fairness as universal principles supported or undermined by algorithms, and with purely social accounts of interpretation.

Its process model explains the outcome as a mutual relationship over time: HR's mandate for fairness and AI procedures each legitimize, shape, and protect the other.

The abstract frames this as a shift in how fairness is enacted in hiring, with expert practices outside HR losing influence as the AI-supported version of fairness took hold.
sources:
- peer_reviewed | Journal of Management Studies | https://doi.org/10.1111/joms.13276 | 2025-08-24
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