Responsible artificial intelligence in human resource management: a conceptual framework for managing ethical and legal risks

Purpose This paper develops a conceptual framework for managing the ethical and legal risks of artificial intelligence (AI) in human resource management (HRM). It extends HRM theory by linking fairness, accountability and transparency to technological adoption. Design/methodology/approach This paper uses a conceptual synthesis based on a systematic review of peer-reviewed literature and selected policy frameworks. It integrates the technology acceptance model (TAM), fairness-accountability-transparency (FAT) principles and stakeholder theory to connect risk domains with adoption mechanisms. Findings The synthesis identifies five ethical and five legal risk domains that shape governance expectations for AI in HRM. The framework explains how ethical governance and legal compliance shape trust, perceived usefulness, perceived risk and responsible adoption intentions. Practical implications The framework provides HR leaders and policymakers with governance guidance for embedding ethical safeguards and legal compliance into AI-enabled HR practices. Originality/value This paper contributes to HRM theory by specifying a governance-to-acceptance pathway for responsible AI in HRM. It links FAT governance perceptions to trust and then to TAM beliefs and responsible adoption intentions within one integrated model.