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 …

In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently
arises given a decision made by an AI system is,``why was the decision made in this …

A central quest in explainable AI relates to understanding the decisions made by (learned)
classifiers. There are three dimensions of this understanding that have been receiving …

In contrast with ad-hoc methods for eXplainable Artificial Intelligence (XAI), formal
explainability offers important guarantees of rigor. However, formal explainability is hindered …

The advances in Machine Learning (ML) in recent years have been both impressive and far-
reaching. However, the deployment of ML models is still impaired by a lack of trust in how …

Boosted trees is a dominant ML model, exhibiting high accuracy. However, boosted trees
are hardly intelligible, and this is a problem whenever they are used in safety-critical …

Purpose: Environmental, social and governance factors have gained significant traction for
investors to evaluate their investment decisions and achieve higher impact investments …

The formal XAI community has studied a plethora of interpretability queries aiming to
understand the classifications made by decision trees. However, a more uniform …

Two types of explanations have been receiving increased attention in the literature when
analyzing the decisions made by classifiers. The first type explains why a decision was …

Infrastructure projects often encounter performance challenges, such as cost overruns and
safety issues, due to complex risk interactions and systemic risks. Existing literature treats …