Artificial Intelligence in Recruitment: A Review of Efficiency Claims, Bias Risks, And the Governance Imperative for HR Practice

Abstract

Artificial intelligence has diffused rapidly through the recruitment function, from automated résumé screening and chatbot-led candidate engagement to algorithmic assessment and predictive analytics, and vendors and adopters advance strong claims about efficiency and quality-of-hire gains. Simultaneously, high-profile failures have made algorithmic hiring a focal case in debates about automated discrimination and its regulation. This paper reviews the multidisciplinary literature on AI in recruitment — spanning human resource management, information systems, computer science research on algorithmic fairness, and the emerging regulatory scholarship — to assess what is credibly known about its benefits, its risks, and the conditions that separate the two. The review finds robust evidence for process-efficiency gains but thin and mixed evidence for quality-of-hire improvement; a well-established taxonomy of bias mechanisms (training data bias, proxy discrimination, and feedback loops) with documented instances in deployed systems; and an emerging governance literature converging on auditability, human oversight, and outcome monitoring as the practices that condition whether adoption helps or harms diversity outcomes. The paper develops a governance-centred framework for HR practice, maps it against incoming regulation including the EU AI Act's classification of employment AI as high-risk, and sets out a research agenda focused on the gap between vendor claims and independently verifiable outcomes.