  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.
