Using AI in HR decision-making: what HR leaders must change to avoid bias, blind automation and loss of trust?

Purpose The purpose of this study is to examine how HR leaders can use artificial intelligence (AI) in decision-making without delegating responsibility to algorithms or undermining employee trust. As AI increasingly shapes recruitment, performance evaluation and talent management, HR leaders face growing pressure to balance efficiency with fairness and accountability. Design/methodology/approach This study adopts a practice-oriented, evidence-informed approach by synthesizing insights from recent research on algorithmic decision-making, human–AI collaboration and ethical HR governance, combined with real-world organizational examples relevant to senior HR leaders. Findings This study finds that AI systems in HR frequently reproduce historical bias and are often perceived by employees as opaque, which weakens trust and perceptions of fairness. AI delivers the greatest value when positioned as decision support rather than decision authority and when HR leaders retain visible accountability for final decisions. Practical implications HR leaders should introduce structured human review stages for high-impact decisions, establish clear AI governance responsibilities and communicate transparently about how AI is used in HR processes. These actions help preserve trust while benefiting from AI-enabled efficiency. Originality/value This study translates current research on AI and HR decision-making into clear, actionable guidance for HR leaders, offering a governance-focused perspective that emphasizes human accountability, ethical responsibility and trust.