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

Decision trees (DTs) epitomize what have become to be known as interpretable machine
learning (ML) models. This is informally motivated by paths in DTs being often much smaller …

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 …

One of the key purposes of eXplainable AI (XAI) is to develop techniques for understanding
predictions made by Machine Learning (ML) models and for assessing how much reliable …

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 …

Recent work has unveiled a theory for reasoning about the decisions made by binary
classifiers: a classifier describes a Boolean function, and the reasons behind an instance …

Quantified Boolean logic results from adding operators to Boolean logic for existentially and
universally quantifying variables. This extends the reach of Boolean logic by enabling a …

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 …

Several queries and scores have recently been proposed to explain individual predictions
over ML models. Examples include queries based on “anchors”, which are parts of an …