Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence (AI) research domain. Differently from other explanation methods, ...

Counterfactual thinking is the process of imagining how an event could have turned out differently (for example: "If Oswald didn’t kill Kennedy, someone else would have").

AI systems can appear to be black boxes – often, even experts don’t know how systems reach their conclusions. The nascent field of “explainable AI” aims to address this problem.

Here, we introduce the first diffusion-driven counterfactual method, DreaMR, to enable fMRI interpretation with high fidelity. DreaMR performs diffusion-based resampling of an input fMRI sample to ...

In this essay, a new counterfactual explanation method is developed to provide explanations for misclassified cases made by black-box models. The proposed method takes a counterfactual explanation ...

In this essay, a new counterfactual explanation method is developed to provide explanations for misclassified cases made by black-box models. The proposed method takes a counterfactual explanation ...

Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and ...

Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and ...