Analyzing employee attrition using explainable AI for strategic HR decision-making

Abstract

Employee attrition and high turnover have become critical challenges across multiple sectors in today’s competitive job market. In response to these issues, organizations increasingly rely on artificial intelligence (AI) to predict employee attrition and design effective retention strategies. This paper explores the application of explainable AI (XAI) to identify potential turnover risks and propose data-driven solutions to address this complex problem. The first section examines the growing impact of employee attrition in specific industries, highlighting its negative consequences for organizational productivity, morale, and financial stability. The second section focuses on AI techniques used to forecast the likelihood of employee departure by analyzing historical data, behavioral patterns, and external factors. Early detection of risk indicators enables proactive and personalized retention interventions. The third section introduces explainable AI approaches that enhance the transparency and interpretability of AI-based predictive models. By integrating XAI into predictive systems, organizations gain deeper insight into the factors driving employee turnover. This interpretability supports human resources (HR) professionals and decision-makers in understanding model outputs and developing targeted retention and recruitment strategies aligned with individual employee needs.