The deployment of systems of artificial intelligence (AI) in high-risk settings warrants the
need for trustworthy AI. This crucial requirement is highlighted by recent EU guidelines and …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

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 …

Abstract Explainable Artificial Intelligence (XAI) is widely considered to be critical for building
trust into the deployment of systems that integrate the use of machine learning (ML) models …

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with
mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of …

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 …

Compilation into propositional languages finds a growing number of practical uses,
including in constraint programming, diagnosis and machine learning (ML), among others …

Abstract We present VeriX (Verified eXplainability), a system for producing optimal robust
explanations and generating counterfactuals along decision boundaries of machine …

The recent criticisms of the robustness of post hoc model approximation explanation
methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations …

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with
well-known model-agnostic explanation approaches, and it is also the case with approaches …