Checks and balances: leveraging artificial intelligence for tri-balance personnel selection systems
通过30个利益相关者访谈的定性分析，提出效率、公平和发言权三平衡模型，为AI在人员选拔中的有效和负责任使用提供框架。
The integration of algorithmic tools in human resource management (HRM) presents both significant opportunities and pressing challenges, particularly in personnel selection, where the adoption of artificial intelligence (AI) tools is rapidly expanding. However, academic research has lagged behind, leaving theoretical and empirical gaps in understanding the implications of AI-driven selection systems. This study employs an interdisciplinary approach, drawing from industrial relations and social psychology, to examine the integration of AI in staffing practices. Through qualitative analysis of 30 stakeholder interviews, it develops an inductive theory identifying efficiency, equity, and voice as core objectives shaping effective and responsible AI-based selection systems. The findings reveal that balancing these objectives requires navigating complex internal and external dynamics, culminating in the proposed tri-balance model. This model offers a framework for aligning stakeholder priorities and optimizing AI adoption. The study concludes by discussing its theoretical contributions, practical implications, and avenues for future research on algorithmic personnel selection.