Human-Centered AI Adoption in Organizational Human Resource Management: Determinants and Implementation Strategies
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Abstract
The increasing adoption of artificial intelligence in organizational management requires effective integration of intelligent decision support, human-centered design, and secure information governance. This study investigates the key determinants affecting human-centered AI adoption in human resource management and develops an implementation framework that combines employee participation, explainable decision mechanisms, and data-governance strategies. The proposed framework analyzes technology acceptance, algorithm transparency, human–AI collaboration, and privacy protection as interconnected components of an intelligent management architecture. Furthermore, a dual-track decision model is introduced to balance automated analytical efficiency with human judgment in high-impact management scenarios. To support trustworthy deployment, a governance structure incorporating hierarchical access control, privacy-preserving computation, ethical review mechanisms, and transparent information management is established. The framework also emphasizes continuous feedback, interpretability, and employee engagement to improve system acceptance and operational reliability. By integrating intelligent information processing, explainable decision support, and secure data governance, the proposed approach provides an engineering-oriented methodology for trustworthy AI deployment and human–AI collaborative management in data-intensive organizational environments.
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