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 …

The complex and opaque decision-making process of machine learning models restrains
the interpretability of predictions and makes them cannot mine results outside of learning …

Counterfactual examples identify how inputs can be altered to change the predicted class of
a classifier, thus opening up the black-box nature of, eg, deep neural networks. We propose …