Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidence

2026-08-31

AI Summary

本文通过基于证据权重的方法，评估特征重要性解释的一致性和稳定性，提供了一种新的视角来评价可解释AI中的特征重要性方法。

Abstract

Feature importance Methods (FIMs) are widely used in Explainable AI to interpret model predictions, yet attribution scores alone often provide limited insight into the underlying reasoning process. In this work, we introduce a novel perspective by embedding FIMs within a hypothesis-testing framework based on Weight of Evidence (WoE). We quantify how strongly the observed evidence supports any given hypothesis on feature importance. The reference hypothesis can stem from domain knowledge, ground truth, or be derived from the FIM itself. This formulation enables a principled evaluation of FIMs, capturing both their alignment with prior knowledge and their variability. We further provide theoretical results linking WoE to attribution variance. Empirical results shows the applicability and flexibility of our strategy analyzing LIME and SHAP explanations in settings with different reference hypotheses. Overall, our framework offers a complementary tool for assessing FIMs through a contrastive, evidence-based lens.

Problem

• Feature Importance Methods
• Explainable AI
• Weight of Evidence

Innovation

• Feature Importance Methods
• Weight of Evidence
• Hypothesis Testing
• Attribution Variance
• Explainable AI