Abstract

The increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) in Human Resource Management (HRM) has led to the implementation of algorithmic performance management systems. These systems promise increased efficiency and objectivity in evaluating employee performance. However, they also raise significant concerns regarding algorithmic bias, fairness, and transparency. This paper critically examines the potential for bias in these systems, analyzing how data biases, flawed algorithms, and lack of human oversight can lead to discriminatory outcomes. The study investigates the impact of algorithmic bias on employee perception of fairness, trust, and engagement. Through a combination of literature review, theoretical analysis, and empirical data collected from a simulated performance evaluation scenario, the paper highlights the challenges associated with implementing unbiased algorithmic performance management systems and proposes recommendations for mitigating these risks, ensuring ethical and equitable application of AI in HRM. The research aims to contribute to the development of fair, transparent, and accountable AI-driven performance management practices that foster a positive and inclusive work environment.