Explainable artificial intelligence in human resource analytics: analysis of performance factors - Kazinets, Aliaksandr N. (Journal of the Belarusian State University. Economics, 2025)
In the context of digitalisation and the growing importance of human capital, explainable artificial intelligence models in human resource analytics are particularly relevant. Using the XGBoost gradient model, the impact of transversal skills and digital competencies on key employee performance indicators is analysed. The SHAP method, which ensures forecast transparency, is used to interpret the contribution of features. A methodology is developed for the stages of open data selection, feature generation, and model interpretation. It is established that factors such as communication skills, digital literacy, and tenure have a significant impact on performance. The managerial consequences of using explainable artificial intelligence to support human resource development decisions are discussed. The importance of interpretable models for increasing trust in algorithms in human resource analytics is emphasised.