Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution

Path-based attribution methods such as Integrated Gradi-ents (IG) are widely adopted for their strong axiomatic properties andeffectiveness in attributing model predictions to input features by inte-grating gradients along a path from a baseline to the input. However,the choice of the attribution path largely affects the quality of explana-tions, and existing approaches rely on fixed or hand-crafted paths thatoften produce noisy or distorted attributions. To address this limitation,we propose Diffusion Integrated Gradients (DiffIG), a novel method thatreformulates path generation as a conditional generative modeling prob-lem. DiffIG first trains a diffusion model to learn a distribution overpaths generated from a Stick-Breaking Process, then employs guidedsampling to embed user guidance during the sampling procedure. Wedemonstrate that DiffIG quantitatively matches or outperforms existingpath-based methods, achieving perceptually aligned explanations. Thiswork introduces a new generative perspective for flexible, inference-timecontrollable Explainable Artificial Intelligence (XAI) methods.