Abstract
There is a growing need for machine learning technologies. In the past, the accuracy of the output results ( prediction results ) of machine learning models was the main focus of attention. Today, however, as machine learning technology is applied to various problem fields, it is required to explain not only the accuracy but also the basis on which the results were obtained. This is because machine learning models themselves are black boxes, and when data is input to a machine learning model, a certain output ( result ) is obtained, but it is difficult for users to understand why hat output ( result ) was obtained. For this background, a number of methods have been proposed to explain the basis of results obtained from machine learning models, and they are called “ eXplainable Artificial Intelligence：XAI ”. A variety of research is being conducted, from basic research to applied research. This paper introduces some of the explanation methods of XAI.
