Four Axiomatic Characterizations of the Integrated Gradients Attribution Method
This work provides theoretical foundations for interpretability in AI, which is crucial for building trust and understanding in black-box models, though it is incremental as it builds on existing axiomatic frameworks.
The paper tackles the problem of explaining deep neural networks by presenting four axiomatic characterizations of the Integrated Gradients attribution method, establishing it as the unique method that satisfies specific sets of principles among a class of attribution methods.
Deep neural networks have produced significant progress among machine learning models in terms of accuracy and functionality, but their inner workings are still largely unknown. Attribution methods seek to shine a light on these "black box" models by indicating how much each input contributed to a model's outputs. The Integrated Gradients (IG) method is a state of the art baseline attribution method in the axiomatic vein, meaning it is designed to conform to particular principles of attributions. We present four axiomatic characterizations of IG, establishing IG as the unique method to satisfy different sets of axioms among a class of attribution methods.