How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?

TL;DR

This paper evaluates the fidelity of popular feature-additive explainers like LIME and SHAP in explaining feature-additive models, revealing their limitations especially with feature interactions across various models and datasets.

Contribution

It provides a systematic evaluation of feature-additive explainers against ground truth derived from model structures, highlighting their shortcomings in real-world scenarios.

Findings

Explainability methods often fail to accurately attribute feature importance.

Performance degrades with complex feature interactions.

All tested explainers show limitations in real-world tasks.

Abstract

Surging interest in deep learning from high-stakes domains has precipitated concern over the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led to an abundance of explanation algorithms for these black boxes. Such post hoc explainers produce human-comprehensible explanations, however, their fidelity with respect to the model is not well understood - explanation evaluation remains one of the most challenging issues in XAI. In this paper, we ask a targeted but important question: can popular feature-additive explainers (e.g., LIME, SHAP, SHAPR, MAPLE, and PDP) explain feature-additive predictors? Herein, we evaluate such explainers on ground truth that is analytically derived from the additive structure of a model. We demonstrate the efficacy of our approach in understanding these explainers applied to symbolic expressions, neural networks, and generalized additive models on thousands of synthetic and several real-world tasks. Our results suggest that all explainers eventually fail to correctly attribute the importance of features, especially when a decision-making process involves feature interactions.