The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it also based on saliency maps. One solution to consider intrinsic interpretability, which does not exhibit drawback of unsoundness. Unfortunately, interpretability can display unwieldy redundancy. Formal explainability represents alternative these non...

Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable. In this paper, we propose DiME, method allowing the generation of counterfactual images using recent diffusion models. By leveraging guided generative process, our proposed methodology shows how use gradients target classifier generate input instances. Further, analyze cu...