An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence

Abstract

Recent studies focus on machine learning (ML) algorithms for predicting employee churn (ECn) to save probable economic loss, technology leakage, and customer and knowledge transference. However, can human resource professionals rely on algorithms for prediction? Can they decide when the process of prediction is not known? Due to the lack of interpretability, ML models' exclusive nature and growing intricacy make it challenging for field experts to comprehend these multifaceted black boxes. To address the concern of interpretability, trust and transparency of black-box predictions, this study explores the application of explainable artificial intelligence (XAI) in identifying the factors that escalate the ECn, analysing the negative impact on productivity, employee morale and financial stability. We propose a predictive model that compares the best two top-performing algorithms based on the performance metrics. Thereafter, we suggest applying an explainable artificial intelligence based on Shapley values, i.e., the Shapley Additive exPlanations approach (SHAP), to identify and compare the feature importance of top-performing algorithms logistic regression and random forest analysis on our dataset. The interpretability of the predictive outcome unboxes the predictions, enhancing trust and facilitating retention strategies.