AI-Driven Employee Performance Evaluation: Building a Fair and Transparent HR Analytics System

Artificial intelligence (AI), machine learning, and explainable AI (XAI) techniques are transforming employee performance evaluation from subjective, periodic appraisals into data-driven, continuous, and transparent systems. While individual technologies offer significant benefits in predictive accuracy and efficiency, their combined potential for achieving both fairness and transparency in HR analytics remains underexplored, particularly when integrated with strategic organizational considerations. This chapter critically examines the convergence of AI-driven predictive modeling, algorithmic bias mitigation, and explainable AI approaches, evaluated through theoretical frameworks, literature synthesis, and empirical illustration using the IBM HR Analytics Employee Attrition & Performance dataset. Strategic analysis frameworks (PESTEL, Porter’s Five Forces, and SWOT) are applied to assess macro- and micro-environmental factors influencing responsible adoption. Findings demonstrate that integrated models can achieve perfect predictive accuracy while eliminating observed demographic bias and providing clear, interpretable explanations via SHAP, thereby addressing long-standing limitations of traditional performance systems. The chapter offers theoretical contributions by unifying bias management, explainability, and strategic HR perspectives into a cohesive framework and provides practical guidance for organizations seeking to implement fair, transparent, and ethically responsible AI-driven performance evaluation systems in the digital era.