This article examines the problem of the «black box» in artificial intelligence systems, focusing on the role of explanation (revealing cause-and-effect relationships) and interpretation (adapting meaning for the audience) in the context of machine learning. The philosophical foundations of these concepts are presented, along with an overview of modern methods in explainable AI (XAI). The article emphasizes the need to develop common perspectives on the issues of “explainability” and “interpretability” as they apply to machine learning models and the solutions they generate.