There is an emerging interest in generating robust counterfactual explanations that would
remain valid if the model is updated or changed even slightly. Towards finding robust …

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …

Abstract Counterfactual Explanations (CEs) have received increasing interest as a major
methodology for explaining neural network classifiers. Usually, CEs for an input-output pair …

Counterfactual explanations (CEs) are advocated as being ideally suited to providing
algorithmic recourse for subjects affected by the predictions of machine learning models …

Given the black box nature of machine learning models, a plethora of explainability methods
have been developed to decipher the factors behind individual decisions. In this paper, we …

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable
machine learning. For a given classifier and an instance classified in an undesired class, its …

Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI
research, providing recourse recommendations for users affected by the decisions of …

Counterfactual explanations suggest what should be different in the input instance to
change the outcome of an AI system. When dealing with counterfactual explanations in the …

Counterfactual (CF) explanations, also known as contrastive explanations and algorithmic
recourses, are popular for explaining machine learning models in high-stakes domains. For …

Due to user demand and recent regulation (GDPR, AI Act), decisions made by AI systems
need to be explained. These decisions are often explainable only post hoc, where …