Robustness and Usefulness in AI Explanation Methods

Abstract

Explainability in machine learning has become incredibly important as machine learning-powered systems become ubiquitous and both regulation and public sentiment begin to demand an understanding of how these systems make decisions. As a result, a number of explanation methods have begun to receive widespread adoption. This work summarizes, compares, and contrasts three popular explanation methods: LIME, SmoothGrad, and SHAP. We evaluate these methods with respect to: robustness, in the sense of sample complexity and stability; understandability, in the sense that provided explanations are consistent with user expectations; and usability, in the sense that the explanations allow for the model to be modified based on the output. This work concludes that current explanation methods are insufficient; that putting faith in and adopting these methods may actually be worse than simply not using them.