The Legitimation-Validation Paradox in AI-Driven Talent Acquisition: A Critical Systematic Review

Hajar Mountassir () and Hicham Benyassine
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Hajar Mountassir: Faculté des sciences juridiques, économiques et sociales, Souissi-Rabat. Université Mohammed V de Rabat - LARCEPEM / Département d'économie et de gestion.
Hicham Benyassine: Faculté des sciences juridiques, économiques et sociales, Souissi-Rabat. Université Mohammed V de Rabat - LARCEPEM / Département d'économie et de gestion.

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Abstract: AI-based recruitment tools are increasingly used to screen, match, assess, and select candidates, yet the evidence supporting their predictive validity remains less developed than their organizational legitimacy and scholarly visibility. This article examines this tension as a legitimation-validation paradox, defined as a condition in which technologies become institutionally accepted while their core performance claims remain insufficiently substantiated. The study conducts a critical systematic review with a theoretical aim, following the PRISMA 2020 protocol and applying controversy mapping rather than conventional thematic synthesis. The corpus comprises 110 publications, including studies identified within the 2015–2024 search window and foundational works retrieved through backward citation searching. Searches were conducted in Scopus, Web of Science, EBSCO Business Source, PsycINFO, IEEE Xplore, JSTOR, Google Scholar, ProQuest Dissertations, and Cairn.info. Grey literature and trade publications were excluded from the database-search evidence base, except when used as contextual or foundational sources identified through citation tracking. The analysis reveals a persistent asymmetry between relatively well-documented efficiency claims and weaker, contested, or insufficiently replicated evidence concerning criterion validity, fairness, and predictive performance. By distinguishing operational efficiency from predictive validity and fairness, the article reorients the AI recruitment debate toward the prior question of independent validation. The findings should be interpreted cautiously, given the predominantly Western and Anglophone composition of the reviewed corpus.

Keywords: Systematic Review; Algorithmic Recruitment; Institutional Isomorphism; Legitimation–Validation Paradox; Talent Acquisition; Artificial Intelligence; ProQuest Artificial Intelligence; Legitimation-Validation Paradox; Systematic Review. Classification JEL: M51; L15; EBSCO Business Source; Isomorphisme institutionnel; IEEE Xplore; JSTOR; Google Scholar; ProQuest Intelligence artificielle Acquisition de talents Paradoxe légitimation-validation Isomorphisme institutionnel Recrutement algorithmique Revue systématique. JEL Classification : M51 O33 M15 D02 J71 L15 PsycINFO; ProQuest Artificial Intelligence Talent Acquisition Legitimation-Validation Paradox Institutional Isomorphism Algorithmic Recruitment Systematic Review. Classification JEL: M51 O33 M15 D02 J71 L15; ProQuest Intelligence artificielle; Acquisition de talents; Paradoxe légitimation-validation; PsycINFO; Recrutement algorithmique; Revue systématique. JEL Classification : M51; O33; M15; D02; J71; L15 PsycINFO (search for similar items in EconPapers)
Date: 2026
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Published in International Journal of Accounting, Finance, Auditing, Management and Economics, 2026, ⟨10.5281/zenodo.20592770⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05655614

DOI: 10.5281/zenodo.20592770

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