Title: Dummy Consistent Path Method for Robust Attribution with Irrelevant Features
Primary area: interpretability and explainable AI
Keywords: path method, input attribution, aumann shapley value, shapley interaction, dummy player axiom, dummy consistency axiom

Abstract: Attribution methods, which explain how individual input features contribute to model predictions, are essential for interpreting deep neural networks (DNNs). In the absence of definitive ground truth, theoretically grounded approaches are critical. Among these, the Shapley value, rooted in game theory, is uniquely desirable as it provides a provably fair allocation of contributions. While existing methods have desirable theoretical properties, they often violate the Dummy Player and Dummy Consistency axioms when applied to rectified DNNs, leading to irrelevant attributions and unreliable feature-interactive contributions due to their shattered gradients problem. To address these limitations, we propose *Dummy Consistent Attribution Path Method* (DCAPM), a novel path-based attribution method that rigorously satisfies these two previously overlooked axioms. DCAPM dynamically constructs an integration path, nullifying spurious attributions from irrelevant feature interactions with dummies to ensure zero attribution at every step, grounded in a principled approximation of the Shapley interaction value. We additionally present a novel metric, grounded in the Dummy Consistency axiom, to quantify dummy consistency. Using this metric, we demonstrate that existing methods systematically fail to satisfy Dummy Consistency. Furthermore, we validate DCAPM on standard sensitivity metrics, showing that an axiomatically consistent approach yields robust and faithful explanations by enhancing sensitivity.

TL;DR: We propose a path-based attribution method that resolves noisy explanations in rectified DNNs by dynamically finding a path that eliminates the influence of irrelevant (dummy) features, resulting in more reliable and consistent attributions.