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 …

Abstract 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 …

Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …

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 …

Counterfactual explanations are gaining prominence within technical, legal, and business
circles as a way to explain the decisions of a machine learning model. These explanations …

Machine learning models have had discernible achievements in a myriad of applications.
However, most of these models are black-boxes, and it is obscure how the decisions are …

We are witnessing a fast development of Artificial Intelligence (AI), but it becomes
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …

Counterfactual explanations can be obtained by identifying the smallest change made to an
input vector to influence a prediction in a positive way from a user's viewpoint; for example …