A novel post-hoc explanation comparison metric and applications
Shreyan Mitra Leilani Gilpin
Explanatory systems make the behavior of machine learning models more
transparent, but are often inconsistent. To quantify the differences between
explanatory systems, this paper presents the Shreyan Distance, a novel metric
based on the weighted difference between ranked feature importance lists
produced by such systems. This paper uses the Shreyan Distance to compare two
explanatory systems, SHAP and LIME, for both regression and classification
learning tasks. Because we find that the average Shreyan Distance varies
significantly between these two tasks, we conclude that consistency between
explainers not only depends on inherent properties of the explainers
themselves, but also the type of learning task. This paper further contributes
the XAISuite library, which integrates the Shreyan distance algorithm into
machine learning pipelines.