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 …

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 …

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 …

The recent criticisms of the robustness of post hoc model approximation explanation
methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations …

Recent years witnessed a number of proposals for the use of the so-called interpretable
models in specific application domains. These include high-risk, but also safety-critical …

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 …

Boosted trees is a dominant ML model, exhibiting high accuracy. However, boosted trees
are hardly intelligible, and this is a problem whenever they are used in safety-critical …

In recent years, the impact of machine learning (ML) and artificial intelligence (AI) in society
has been absolutely remarkable. This impact is expected to continue in the foreseeable …

The formal XAI community has studied a plethora of interpretability queries aiming to
understand the classifications made by decision trees. However, a more uniform …

Abductive explanations (AXp's) are widely used for understanding decisions of classifiers.
Existing definitions are suitable when features are independent. However, we show that …