The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective
explanations for black-box classifiers. The existing literature lists many desirable properties …

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 interdisciplinary field of explainable artificial intelligence (XAI) aims to foster human
understanding of black-box machine learning models through explanation methods …

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has
emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian …

Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

As artificial intelligence and machine learning algorithms make further inroads into society,
calls are increasing from multiple stakeholders for these algorithms to explain their outputs …

Evaluating local explanation methods is a difficult task due to the lack of a shared and
universally accepted definition of explanation. In the literature, one of the most common …

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 …

In recent years, there has been an explosion of AI research on counterfactual explanations
as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer …

Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …