Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making

Gabriel Marín Díaz (), José Javier Galán Hernández and José Luis Galdón Salvador
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Gabriel Marín Díaz: Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain
José Javier Galán Hernández: Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain
José Luis Galdón Salvador: Management Department, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain

Mathematics, 2023, vol. 11, issue 22, 1-25

Abstract: Employee attrition and high turnover have become critical challenges faced by various sectors in today’s competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict employee attrition and implement effective retention strategies. This paper delves into the application of explainable AI (XAI) in identifying potential employee turnover and devising data-driven solutions to address this complex problem. The first part of the paper examines the escalating problem of employee attrition in specific industries, analyzing the detrimental impact on organizational productivity, morale, and financial stability. The second section focuses on the utilization of AI techniques to predict employee attrition. AI can analyze historical data, employee behavior, and various external factors to forecast the likelihood of an employee leaving an organization. By identifying early warning signs, businesses can intervene proactively and implement personalized retention efforts. The third part introduces explainable AI techniques which enhance the transparency and interpretability of AI models. By incorporating these methods into AI-based predictive systems, organizations gain deeper insights into the factors driving employee turnover. This interpretability enables human resources (HR) professionals and decision-makers to understand the model’s predictions and facilitates the development of targeted retention and recruitment strategies that align with individual employee needs.

Keywords: XAI; interpretability; decision-making; employee attrition; machine learning; human resources (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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