Explainable Artificial Intelligence in Human Resources: a Computational Study

The application of Machine Learning in Human Resources is becoming increasingly common. The Human Resources industry relies on Machine Learning to automate processes, improve efficiency, and enhance decision making. However, using Machine Learning in Human Resources is not without its challenges. One of these challenges is the lack of interpretability. Interpretability is essential in Human Resources because many of the decisions made in the field have a direct impact on people's lives. As such, these decisions must be made transparently and understandably. In this paper, we apply a model-agnostic technique for providing post-hoc explanations, known as Anchors, to a real-world dataset from the Human Resources industry. The dataset was used to create a predictive pipeline for employee attrition. The result is a Human Capital Management system capable of explaining the reasons behind employee attrition, allowing the Human Resource professionals to enact retention policies promptly. Moreover, the results suggest that Anchors can be used to create a prescriptive pipeline that can be used to explain the reasons behind every single decision to leave the company, as they are easily interpretable by a non-expert. This system has the advantage of allowing the Decision Maker to act in a prescriptive way and retain valuable resources.