Robust Attribution Regularization
This addresses the need for trustworthy machine learning by improving interpretation robustness, though it is incremental as it builds on existing axiomatic attribution methods.
The paper tackles the problem of training neural networks to produce robust interpretations of their predictions by proposing training objectives for robust Integrated Gradients attributions, with experiments showing effectiveness but also highlighting challenges in optimization or architecture.
An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions. We take a step towards solving this problem through the lens of axiomatic attribution of neural networks. Our theory is grounded in the recent work, Integrated Gradients (IG), in axiomatically attributing a neural network's output change to its input change. We propose training objectives in classic robust optimization models to achieve robust IG attributions. Our objectives give principled generalizations of previous objectives designed for robust predictions, and they naturally degenerate to classic soft-margin training for one-layer neural networks. We also generalize previous theory and prove that the objectives for different robust optimization models are closely related. Experiments demonstrate the effectiveness of our method, and also point to intriguing problems which hint at the need for better optimization techniques or better neural network architectures for robust attribution training.