As black box explanations are increasingly being employed to establish model credibility in
high stakes settings, it is important to ensure that these explanations are accurate and …

Recent work has shown that some common machine learning classifiers can be compiled
into Boolean circuits that have the same input-output behavior. We present a theory for …

In recent years, there has been an increasing interest in exploiting logically specified
background knowledge in order to obtain neural models (i) with a better performance,(ii) …

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

In spite of several claims stating that some models are more interpretable than others--eg,"
linear models are more interpretable than deep neural networks"--we still lack a principled …

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 …

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 …

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 …

Recent work proposed the computation of so-called PI-explanations of Naive Bayes
Classifiers (NBCs). PI-explanations are subset-minimal sets of feature-value pairs that are …

Deep neural networks have emerged as a widely used and effective means for tackling
complex, real-world problems. However, a major obstacle in applying them to safety-critical …