Problematizing the role of artificial intelligence in hiring and organizational inequalities: A multidisciplinary review

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; and law. Using a hybrid review approach that combines scoping and problematizing review methods, we examine the implications of algorithmic hiring for organizational inequalities. Our review identifies a multidisciplinary discussion marked by asymmetries in how key concerns are conceptualized; a clear and heightened potential for AI to conceal inequalities in hiring processes; and contestation over the regulation of algorithmic hiring. Building on Acker's (2006) framework of 'inequality regimes', we propose the concept of <i>algorithmically-mediated inequality regimes</i> to highlight AI's capacity for concealing and reproducing inequalities in hiring through enhanced <i>algorithmic invisibility</i> and the growing <i>legitimacy</i> of AI solutions. We propose an agenda for future research, policy, and practice, emphasizing the need for an interdisciplinary 'chain of knowledge' and a multi-stakeholder 'chain of responsibility' in AI application and regulation.