In recent years, considerable progress has been made in the field of artificial intelligence and machine learning. This progress allows us to solve increasingly complex problems, but it also requires providing appropriate explanations to understand the actions taken by AI. For this purpose, research into the development of Explainable Artificial Intelligence has been initiated and interest in this topic is constantly growing. This review of XAI methods includes a justification for the need to introduce solutions to explain artificial intelligence models, describes the differences between various methods and presents example method/s that work in different cases. The purpose of this paper is to solve a real problem occurring in heavy industry. The third chapter describes the challenges to be faced, the solution developed and the results of the work. The entire study concludes with a summary of the research findings.
