Conventional employee performance reviews are based on recurring evaluations and subjective assessments, which frequently result in prejudice, inefficiencies, and inconsistent workforce management. Although machine learning and predictive analytics have demonstrated promise in HR analytics, little is known about how they may be used in performance evaluation. The accuracy, objectivity, and real-time decision-making in employee assessments can be improved by using sophisticated machine learning models, such as decision trees, random forests, support vector machines (SVM), and deep learning, according to this study.This study focuses on examining key performance indicators (KPIs), such as productivity measures, peer feedback, and behavioural patterns, to close the gap between conventional evaluation techniques and AI-driven insights. It also looks at how explainable AI (XAI) may guarantee fairness and openness in automated performance evaluations. The results show that incorporating predictive analytics into frameworks for performance evaluations can result in better workforce efficiency, less bias, and more data-driven decision-making. To implement AI-powered HR analytics, this study offers firms a methodical strategy that facilitates more equitable evaluations, improved talent retention, and smart workforce planning.
Predictive Analytics, Machine Learning, Employee Performance Evaluation, Human Resource Analytics, Explainable Artificial Intelligence, Workforce Optimization, and Talent Management are Some of the Index Terms.
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