Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

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 …

In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …

The complexity of state-of-the-art modeling techniques for image classification impedes the
ability to explain model predictions in an interpretable way. A counterfactual explanation …

In this paper we propose a new algorithm, named NICE, to generate counterfactual
explanations for tabular data that specifically takes into account algorithmic requirements …

In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a
user the predictions of a trained decision model by indicating the modifications to be made …

Black-box machine learning models are used in an increasing number of high-stakes
domains, and this creates a growing need for Explainable AI (XAI). However, the use of XAI …

The counterfactual approach to explainable AI (XAI) seeks to provide understanding of AI
systems through the provision of counterfactual explanations. In a recent systematic review …

The growing prevalence of artificial decision systems has prompted a keen interest in their
efficiency, yet this progress is accompanied by their inherent complexity. This poses a …