USING EXPLAINABLE AI (XAI) ALGORITHMS TO INTERPRET THE RESULTS OF ANALYTICAL MODELS IN MANAGEMENT DECISION-MAKING

Abstract

This article discusses the application of Explainable methods. Artificial Intelligence (XAI) for interpreting the results of analytical models in the context of management decisions. This paper analyzes modern approaches to explainable AI, including interpretable models ( ante-hoc ) and post-hoc methods for " black -box" machine learning, such as LIME, SHAP, and visualization techniques. Recommendations for integrating XAI into management practice are presented. This paper is intended for researchers and practitioners interested in improving the effectiveness and validity of data-driven decisions.