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TRUVACE RECORD VERSION
record: TRV-2026-0415
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T10:36:31.840508Z
status: published
lens: p_space
sector: labor
headline: Problematizing the role of artificial intelligence in hiring and organizational inequalities: A multidisciplinary review
dek: What are the implications of the growing use of artificial intelligence (AI) in recruitment and hiring for organizational inequalities? While advocates suggest that AI is a groundbreaking tool that can enhance hiring precision, efficiency, diversity and fit, critics raise serious concerns around bias, fairness, and privacy. This review article critically advances this debate by drawing on diverse scholarship across computing and data sciences; human resource, management, and organization studies; social sciences…
gain_title: (none)
problem_title: AI use in recruitment and hiring creates a heightened risk of concealing and reproducing organizational inequalities through algorithmic invisibility and growing legitimacy of AI solutions.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: AI use in recruitment and hiring creates a heightened risk of concealing and reproducing organizational inequalities through algorithmic invisibility and growing legitimacy of AI solutions.
problem_evidence: clear and heightened potential for AI to conceal inequalities in hiring processes | concealing and reproducing inequalities in hiring through enhanced algorithmic invisibility
quick_read: A December 2025 review in Human Relations examined the growing use of artificial intelligence in recruitment and hiring and its implications for organizational inequalities. Using a hybrid scoping and problematizing approach, the authors synthesized multidisciplinary literature and found asymmetries in conceptualization, a heightened potential for AI to conceal inequalities, and ongoing contestation over regulation.

The concealment matters because hiring shapes access to jobs and organizational stratification, and algorithmic invisibility combined with legitimacy of AI solutions may make inequalities harder to detect and challenge. What remains uncertain is how to operationalize accountability across disciplines and stakeholders, and whether proposed chains of knowledge and responsibility will translate into enforceable practice.
limitation: 
tag: Evidence-backed problem
key_points: Review examines AI in recruitment and hiring across computing, HR, management, social sciences and law using hybrid scoping and problematizing methods. | Authors identify asymmetries in how bias, fairness and privacy concerns are conceptualized across disciplines. | Proposes concept of 'algorithmically-mediated inequality regimes' building on Acker's framework to describe enhanced algorithmic invisibility and legitimacy of AI solutions.
rundown: The review draws on scholarship across computing and data sciences; human resource, management, and organization studies; social sciences; and law, noting advocates claim AI can enhance hiring precision, efficiency, diversity and fit while critics raise bias, fairness and privacy concerns.

Findings point to contestation over regulation of algorithmic hiring and call for an interdisciplinary 'chain of knowledge' and multi-stakeholder 'chain of responsibility' in application and regulation.
sources:
- peer_reviewed | Human Relations | https://doi.org/10.1177/00187267251403902 | 2025-12-30
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