Representative Approaches and Recent Advances in Explainable AI

Abstract

Explainable artificial intelligence (XAI) is a research field on the techniques which aim to reduce the opacity of high-performance machine learning models, including deep neural networks and ensemble models, and it has been paid much attention especially in medicine. In this article, I first review several early attempts related to XAI in the literature of artificial intelligence. Then, key concepts, representative techniques, and open issues in XAI are discussed in depth. Finally I also present several advances in the recent XAI research.