The Algorithm-Mediated Inequality Regime in Hiring: A Systematic Review and Integrative Framework

Abstract

AI-driven hiring has become widespread, yet concerns about fairness persist. Technical and behavioral research have evolved in largely disconnected streams. This paper conducts a systematic review following the PRISMA 2020 guidelines, synthesizing 50 studies to propose an “algorithm-mediated inequality regime” framework that integrates technical analyses of bias, applicant fairness perceptions, and ethical critiques. The framework captures how algorithmic hiring systems actively produce and legitimize inequality. Our findings reveal that data bias, algorithmic bias and human-in-loop bias map respectively onto Acker’s mechanisms of invisibility, legitimation, and control, forming a self-reinforcing cycle. Existing governance strategies, including technical fixes, organizational oversight, and legal-institutional mandates, operate within the prevailing inequality regime and fail to address underlying power asymmetries. The study extends organizational inequality theory into algorithmic contexts and offers practitioners a systemic diagnostic tool for auditing and reforming AI-enabled hiring.