Explainable Artificial Intelligence Methods Based on Feature Space Analysis

Abstract

In the 21st century, mankind is actively introducing machine learning and artificial intelligence into all spheres of life. But most modern algorithms output the final result of the calculations without revealing the details of obtaining the result, which is the reason for some skepticism towards it. To correct this situation, there is a need to use understandable machine learning methods that increase the transparency of use and the level of trust of people. The work reviews existing solutions to this problem, and also draws a conclusion on the effectiveness of a particular algorithm. Based on the results of the article, ways to further develop the work are proposed.