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 …

Explainable artificial intelligence (XAI) represents arguably one of the most crucial
challenges being faced by the area of AI these days. Although the majority of approaches to …

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 …

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 …

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite
their importance, these notions have been conceptually underdeveloped and inconsistently …

Abstract Explanations of Machine Learning (ML) models often address a question. Such
explanations can be related with selecting feature-value pairs which are sufficient for the …

Improving trust in decisions made by classification models is becoming crucial for the
acceptance of automated systems, and an important way of doing that is by providing …

Neural networks are increasingly employed in safety-critical domains. This has prompted
interest in verifying or certifying logically encoded properties of neural networks. Prior work …

Tree ensembles (TEs) denote a prevalent machine learning model that do not offer
guarantees of interpretability, that represent a challenge from the perspective of explainable …