Retention Is All You Need
This addresses the need for interpretable AI in HR to reduce turnover, but it is incremental as it applies existing explainability methods to a specific domain.
The paper tackles the problem of high employee attrition by developing an explainable AI system (HR-DSS) that uses SHAP and natural language explanations to interpret predictions from machine learning models, enabling HR to make informed decisions to convert attrition into retention.
Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed to analyze attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propose the HR-DSS approach, which stands for Human Resource (HR) Decision Support System, and uses explainable AI for employee attrition problems. The system is designed to assist HR departments in interpreting the predictions provided by machine learning models. In our experiments, we employ eight machine learning models to provide predictions. We further process the results achieved by the best-performing model by the SHAP explainability process and use the SHAP values to generate natural language explanations which can be valuable for HR. Furthermore, using "What-if-analysis", we aim to observe plausible causes for attrition of an individual employee. The results show that by adjusting the specific dominant features of each individual, employee attrition can turn into employee retention through informative business decisions.