The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

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 …

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 …

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 …

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 …

Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers.
Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for …

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 …