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 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 methods are playing a vital role in combating ever-evolving threats in the
cybersecurity domain. Explanation methods that shed light on the decision process of black …

The growing availability of time series data has increased the usage of classifiers for this
data type. Unfortunately, state-of-the-art time series classifiers are black-box models and …

Counterfactual explanations can provide sample-based explanations of features required to
modify from the original sample to change the classification result from an undesired state to …

In machine learning applications, there is a need to obtain predictive models of high
performance and, most importantly, to allow end-users and practitioners to understand and …

Counterfactual explanations represent the minimal change to a data sample that alters its
predicted classification, typically from an unfavorable initial class to a desired target class …

We tackle the issue of anomaly detection for multivariate functional data in a supervised
setting. Deep learning applied to multivariate time series has become common nowadays …

Counterfactual Explanations (CEs) help address the question: How can the factors that
influence the prediction of a predictive model be changed to achieve a more favorable …

Artificial Intelligence decision-making systems have dramatically increased their predictive
performance in recent years, beating humans in many different specific tasks. However, with …