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 …

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 …

In many classification tasks there is a requirement of monotonicity. Concretely, if all else
remains constant, increasing (resp. ádecreasing) the value of one or more features must not …

Trustable explanations of machine learning (ML) models are vital in high-risk uses of
artificial intelligence (AI). Apart from the computation of trustable explanations, a number of …

This paper develops a rigorous argument for why the use of Shapley values in explainable
AI (XAI) will necessarily yield provably misleading information about the relative importance …

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

Recent work has shown that not only decision trees (DTs) may not be interpretable but also
proposed a polynomial-time algorithm for computing one PI-explanation of a DT. This paper …

Decision trees have long been recognized as models of choice in sensitive applications
where interpretability is of paramount importance. In this paper, we examine the …