SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

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,

Read original source ↗ Open in workspace

recordType

paper

region

Global