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Artificial Intelligence AI-Powered Employee Performance Evaluation

Author

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  • Dr Lai Mun Keong

    (Tunku Abdul Rahman University of Management & Technology, Malaysia)

  • Dr Chok Nyen Vui

    (Manipal GlobalNxt University, Malaysia)

  • Dr Lee Sook Ling

    (Tunku Abdul Rahman University of Management & Technology, Malaysia)

Abstract

In recent years, the integration of artificial intelligence (AI) into human resource management has revolutionized various HR functions, including employee performance evaluation and feedback mechanisms. This research investigates the impact of AI-powered performance evaluation systems on accuracy, fairness, and employee perceptions. By leveraging machine learning algorithms and advanced data analytics, AI-driven evaluation tools promise to provide more objective and comprehensive assessments of employee performance. This study explores the extent to which AI can enhance the accuracy of performance evaluations by minimizing human biases and errors. Additionally, it examines the fairness of AI-driven evaluations, addressing concerns related to algorithmic transparency and potential biases inherent in AI systems. Through a mixed-methods approach, including surveys and interviews with employees and HR professionals, the research captures employee perceptions of AI-powered performance evaluations, assessing their trust in these systems and their perceived effectiveness. The findings aim to provide valuable insights for organizations considering the adoption of AI in their performance management processes, highlighting the benefits and challenges associated with this technological advancement. This study contributes to the growing body of literature on AI in HR, offering practical recommendations for ensuring the ethical and effective implementation of AI-driven performance evaluation systems.

Suggested Citation

  • Dr Lai Mun Keong & Dr Chok Nyen Vui & Dr Lee Sook Ling, 2025. "Artificial Intelligence AI-Powered Employee Performance Evaluation," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(2), pages 1352-1365, February.
  • Handle: RePEc:bcp:journl:v:9:y:2025:i:2:p:1352-1365
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