Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

As machine learning (ML) models and datasets increase in complexity, the demand for
methods that enhance explainability and interpretability becomes paramount. Prototypes, by …

Counterfactual explanations have been argued to be one of the most intuitive forms of
explanation. They are typically defined as a minimal set of edits on a given data sample that …

Deep learning models have achieved impressive performance in various tasks, but they are
usually opaque with regards to their inner complex operation, obfuscating the reasons for …

Machine learning (ML) based systems have been suffering a lack of interpretability. To
address this problem, counterfactual explanations (CEs) have been proposed. CEs are …

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-
markers indicative of the onset and progress of respiratory abnormalities/conditions has …

Counterfactuals have been established as a popular explainability technique which
leverages a set of minimal edits to alter the prediction of a classifier. When considering …

Evaluation of generative models has been an underrepresented field despite the surge of
generative architectures. Most recent models are evaluated upon rather obsolete metrics …

Counterfactual explanations (CEs) based on concepts are explanations that consider
alternative scenarios to understand which high-level semantic features contributed to …

Counterfactuals have been established as a popular explainability technique which
leverages a set of minimal edits to alter the prediction of a classifier. When considering …