AI-Powered Employee Performance Evaluation Systems in HR Management

Abstract

Employee performance evaluation is essential in human resource management, but existing systems are typically subjective and inefficient, resulting in skewed results and employee dissatisfaction. The paper addresses these limitations by introducing an AI-powered employee performance rating system. The system uses data-driven insights and powerful algorithms to provide objective assessments, improving objectivity, fairness, and efficiency in the review process. The proposed system provides real-time feedback and insights by combining extensive data collection and integration, Machine learning (ML) model selection, NLP analysis, and explainable AI methodologies, allowing HR managers to make educated decisions and successfully support employee development. The results and analysis show that the AI-powered system outperforms conventional approaches, with accuracy values ranging from 0.78 to 0.85, precision values from 0.79 to 0.86, and recall values ranging from 0.76 to 0.84. These results demonstrate AI technology's potential to promote organizational success by improving performance evaluation systems, encouraging staff development, and gaining a competitive advantage in the marketplace.

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