Path-Based Visual Explanation

Abstract

The ability to explain the behavior of a Machine Learning (ML) model as a black box to people is becoming essential due to wide usage of ML applications in critical areas ranging from medicine to commerce. Case-Based Reasoning (CBR) received a special interest among other methods of providing explanations for model decisions due to the fact that it can easily be paired with a black box and then can propose a post-hoc explanation framework. In this paper, we propose a CBR-Based method to not only explain a model decision but also provide recommendations to the user in an easily understandable visual interface. Our evaluation of the method in a user study shows interesting results.