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 …

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

As the range of decisions made by Artificial Intelligence (AI) expands, the need for
Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific …

The use of counterfactual explanations (CFXs) is an increasingly popular explanation
strategy for machine learning models. However, recent studies have shown that these …

Recently, there has been a growing demand for the deployment of Explainable Artificial
Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods …

Machine learning (ML) recourse techniques are increasingly used in high-stakes domains,
providing end users with actions to alter ML predictions, but they assume ML developers …

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 …

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