The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

Dialogue systems deliver a more natural mean of communication between humans and
machines when compared to traditional systems. Beyond input/output components that …

Explaining how two machine learning classification models differ in their behaviour is
gaining significance in eXplainable AI, given the increasing diffusion of learning-based …

We investigate a formalism for the conditions of a successful explanation of AI. We consider
“success” to depend not only on what information the explanation contains, but also on what …

The last decades have seen a revolution in autonomous robotics. Deep learning
approaches and their hardware implementations have made it possible to endow robots …

Explaining black-box classification models is a hot topic in AI, with the overall goal of
improving trust in decisions made by such models. Several works have been done and …

In many real-world scenarios, agents are involved in optimization problems. Since most of
these scenarios are over-constrained, optimal solutions do not always satisfy all agents …

The choices made by autonomous robots in social settings bear consequences for humans
and their presumptions of robot behavior. Explanations can serve to alleviate detrimental …

The multimorbidity problem involves the identification and mitigation of adverse interactions
that occur when multiple computer interpretable guidelines are applied concurrently to …

The trade-offs between different desirable plan properties--eg PDDL temporal plan
preferences--are often difficult to understand. Recent work addresses this by iterative …