Article
Towards a Characterisation of Algorithmic Human Resource Management: Exploring the Duality of Efficiency and Equity
The growing integration of artificial intelligence and algorithmic systems into Human Resource Management (HRM) has transformed organisational decision-making processes while simultaneously intensifying concerns surrounding fairness, transparency, and ethical accountability. Although existing scholarship has extensively examined the operational benefits of algorithmic HRM, comparatively limited attention has been devoted to understanding how organisations can balance algorithmic efficiency with human-centred equity in consequential employment decisions. Addressing this gap, the present study develops a conceptual characterisation of Algorithmic Human Resource Management through the integration of socio technical systems theory, augmented decision-making theory, and the algorithmic fairness literature. Methodologically, the study adopts an integrative literature review approach supplemented by comparative case analysis of three widely discussed organisational cases: Amazon’s abandoned AI recruitment system, Unilever’s hybrid AI-assisted hiring model, and HireVue’s algorithmic video interview platform. These cases are further contextualised through a theoretically constructed vignette designed to examine the limits of narrow algorithmic filtering and the continuing relevance of human evaluative judgment. Through this multi-layered analysis, the paper investigates the governance structures required for effective human–AI integration in HRM and explores the broader tension between predictive efficiency and procedural equity.
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