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cs.LG2026
Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidence
Eddie Conti, Claudio Daka, Álvaro Parafita +3
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 r…
cs.LG2026
Measuring Explainer Stability via Attribution Separability
Eddie Conti, Álvaro Parafita, Axel Brando
Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models. However, most methods can produce variable attribution scor…