Can governance, ethics, and system design make AI in HR truly trustworthy?
TL;DR
Investigating the governance of AI systems in HR through four questions shows near-universal support for governance frameworks, with trust significantly associated with professional role, AI familiarity, and governance orientation.
Abstract
AI is rapidly transforming HRM, with organizations and universities adopting AI systems to recruit, evaluate, and analyse their workforces. While these technologies promise efficiency, flaws or biases in algorithms can affect individuals' careers and organizational performance, turning technological improvement into a matter of trust. Without proper governance, AI risks perpetuating bias and undermining accountability. Yet current research focuses largely on efficiency and cost savings, and the lack of integration between HRM and IT perspectives means organizations often implement AI without considering how governance structures and design decisions affect people. This study addresses that gap by investigating the governance of AI systems in HR through four questions: how governance structures shape trust, what role ethical frameworks play in mitigating bias, how system design influences transparency and accountability, and whether demographic and professional differences shape trust. Applying a socio-technical systems analysis to survey data from organizational and higher education participants, the findings show near-universal support for governance frameworks, with trust significantly associated with professional role, AI familiarity, and governance orientation.