Explainable artificial intelligence and methods for interpreting results

Artificial intelligence systems are used in many areas of human life support for example finance or medicine. Every year intelligent systems process more and more data and make more and more decisions. All these decisions have an increasing impact on the fate of people. The corner-stone is a distrust of completely non-human, autonomous artificial intelligence systems. The key to distrust lies in the misunderstanding of why intelligent systems make this or that decision, based on what beliefs such systems operate (and whether they have their own beliefs or only those that were given to them by the developers). To solve the problem of “distrust” in such sys-tems, the methods of explainable artificial intelligence have been used. This article provides a brief overview of the most popular methods in the academic environment such methods as PDP, SHAP, LIME, DeepLIFT, permutation importance, ICE plots. Practical exercises demonstrate the ease of application of PDP and SHAP methods, as well as the convenience of "reading" the graphical results of these methods, which explain the constructed decision tree model and ran-dom forest model on the example of a small set of sales data

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