Abstract
The integration of Artificial Intelligence (AI) into employee performance management (EPM) represents a substantial shift in strategic human resource management. This study examines the multi-faceted impact of Algorithmic Performance Evaluation (APE) systems on employee trust, engagement, and productivity by analyzing secondary data collected from current academic literature, industrial case reviews, and theoretical frameworks. The analysis reveals a complex dynamic: while AI enhances objectivity, minimizes human tracking errors, and enables continuous, real-time feedback loops, it simultaneously presents risks regarding algorithmic bias, psychological strain from constant monitoring, and the "black-box" opacity problem. The findings indicate that employee trust and engagement depend heavily on structural system transparency and the strategic preservation of human intervention. The study concludes with actionable guidelines for establishing a balanced, hybrid workplace evaluation framework.
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