Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
This work addresses interpretability issues in explainable AI for models with non-linear interactions, though it appears incremental as an extension of existing frameworks.
The paper tackles the challenge of interpreting feature attributions in machine learning models with complex feature interactions by proposing a general theory of higher-order feature attribution based on Integrated Gradients. It establishes theoretical connections to statistics and topological signal processing and validates the approach with examples.
Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but becomes less direct when the predictive model involves interactions such as multiplicative relationships or joint feature contributions. In this work, we propose a general theory of higher-order feature attribution, which we develop on the foundation of Integrated Gradients (IG). This work extends existing frameworks in the literature on explainable AI. When using IG as the method of feature attribution, we discover natural connections to statistics and topological signal processing. We provide several theoretical results that establish the theory, and we validate our theory on a few examples.