A Dual-Perspective Approach to Evaluating Feature Attribution Methods
Yawei Li Yang Zhang Kenji Kawaguchi Ashkan Khakzar Bernd Bischl Mina Rezaei
Feature attribution methods attempt to explain neural network predictions by
identifying relevant features. However, establishing a cohesive framework for
assessing feature attribution remains a challenge. There are several views
through which we can evaluate attributions. One principal lens is to observe
the effect of perturbing attributed features on the model's behavior (i.e.,
faithfulness). While providing useful insights, existing faithfulness
evaluations suffer from shortcomings that we reveal in this paper. In this
work, we propose two new perspectives within the faithfulness paradigm that
reveal intuitive properties: soundness and completeness. Soundness assesses the
degree to which attributed features are truly predictive features, while
completeness examines how well the resulting attribution reveals all the
predictive features. The two perspectives are based on a firm mathematical
foundation and provide quantitative metrics that are computable through
efficient algorithms. We apply these metrics to mainstream attribution methods,
offering a novel lens through which to analyze and compare feature attribution
methods.