IG2: Integrated Gradient on Iterative Gradient Path for Feature Attribution

Feature attribution explains Artificial Intelligence (AI) at the instance level by providing importance scores of input features' contributions to model prediction. Integrated Gradients (IG) is a prominent path attribution method for deep neural networks, involving the integration of gradients along a path from the explained input (explicand) to a counterfactual instance (baseline). Current IG variants primarily focus on the gradient of explicand's output. However, our research indicates that the gradient of the counterfactual output significantly affects feature attribution as well. To achieve this, we propose I terative G radient path I ntegrated G radients (IG 2 ), considering both gradients. IG 2 incorporates the counterfactual gradient iteratively into the integration path, generating a novel path ( GradPath ) and a novel baseline ( GradCF ). These two novel IG components effectively address the issues of attribution noise and arbitrary baseline choice in earlier IG methods. IG 2 , as a path method, satisfies many desirable axioms, which are theoretically justified in the paper. Experimental results on XAI benchmark, ImageNet, MNIST, TREC questions answering, wafer-map failure patterns, and CelebA face attributes validate that IG 2 delivers superior feature attributions compared to the state-of-the-art techniques.