DOI RECORD
Socio-materiality taxonomy of risks arising from HCAI systems in human resource management: a mixed-methods design
Abstract
Human-Centered Artificial Intelligence (HCAI) is increasingly reshaping human resource management (HRM) by embedding intelligent systems into HR processes. While HCAI promises enhanced decision quality and operational efficiency, its adoption also introduces complex and interconnected risks that extend beyond purely technological concerns. This study aims to identify and systematize the main risks associated with HCAI implementation in HRM through a mixed-methods design combining a systematic literature review (SLR) and Gioia-based qualitative analysis. Guided by a socio-materiality perspective, the findings reveal three interrelated risk categories – material, human, and contextual – highlighting how vulnerabilities emerge from the interaction between intelligent systems, human actors, organizational structures, and the environment. The study contributes to HRM literature by conceptualizing HCAI risks as systemic multilevel phenomena, offering theoretical insights and practical implications and recommendations for the responsible design and governance of HCAI systems within HRM.