Algorithmic and AI-augmented Human Resource Management: A Review of Recruitment, Predictive Attrition Analytics, People Analytics, and Algorithmic Management Mechanisms

Abstract

Organizations increasingly delegate recruitment screening, attrition forecasting, workforce planning, and even task allocation to algorithmic and machine-learning systems, a shift that has outpaced the development of a unified evidence base comparing these mechanisms on efficiency, fairness, and employee-experience grounds. This review synthesizes peer-reviewed and conference literature on four major families of algorithmic Human Resource Management (HRM) practice — algorithmic recruitment and screening, predictive employee-attrition analytics, descriptive-to-prescriptive people analytics, and algorithmic management on gig and platform work — and compares them on reported efficiency gains, fairness risk, and organizational maturity. A structured narrative review was conducted across IEEE Xplore, SpringerLink, Elsevier ScienceDirect, Emerald, Wiley, and Nature Portfolio journals, screening peer-reviewed studies reporting empirical or conceptual evidence on algorithmic HRM mechanisms published between 2005 and 2026. 33 sources are synthesized into comparative tables spanning mechanism, reported outcome, and principal limitation, alongside six illustrative computed figures benchmarking adoption trajectories, attrition-classifier learning behavior, efficiency-versus-fairness-risk positioning, and reported milestones over 2011–2025. No single reviewed mechanism simultaneously maximizes efficiency, minimizes fairness risk, and sustains employee trust; predictive attrition analytics and maturity-staged people analytics currently offer the most favorable balance, while algorithmic recruitment and gig-platform algorithmic management report the largest efficiency gains alongside the highest documented fairness and governance risk, identifying human-centered algorithmic governance as the central open problem for the field.