A substantial amount of research has been carried out in Explainable Artificial Intelligence
(XAI) models, especially in those which explain the deep architectures of neural networks. A …

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 …

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 …

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

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 …

Circuit representations are becoming the lingua franca to express and reason about
tractable generative and discriminative models. In this paper, we show how complex …

Abstract We present VeriX (Verified eXplainability), a system for producing optimal robust
explanations and generating counterfactuals along decision boundaries of machine …

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with
well-known model-agnostic explanation approaches, and it is also the case with approaches …

The complete reason behind a decision is a Boolean formula that characterizes why the
decision was made. This recently introduced notion has a number of applications, which …

A wide variety of model explanation approaches have been proposed in recent years, all
guided by very different rationales and heuristics. In this paper, we take a new route and cast …