Explainable AI and Its Reliability

Abstract

One major challenge in using machine learning is that models often work like “black boxes”, that is, it is hard for humans to understand how they make decisions. To tackle this issue, there has been growing interest in research focused on making machine learning models more explainable. In this paper, we review some of the main approaches to model explanation. Recently, however, studies have shown that these explanation methods may not always be reliable. We also reviews recent research on the reliability of these explanations.