This paper presents objective metrics for how explainable artificial intelligence (XAI) can be
quantified. Through an overview of current trends, we show that many explanations are …

We introduce four principles for explainable artificial intelligence (AI) that comprise
fundamental properties for explainable AI systems. We propose that explainable AI systems …

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

The field of explainable artificial intelligence (XAI) is gaining increasing importance in recent
years. As a consequence, several surveys have been published to explore the current state …

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to
provide transparency, fairness, accuracy, generality, and comprehensibility to the results …

We are witnessing a fast development of Artificial Intelligence (AI), but it becomes
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …

In this paper, we present the potential of Explainable Artificial Intelligence methods for
decision support in medical image analysis scenarios. Using three types of explainable …

There are emerging concerns about the Fairness, Accountability, Transparency, and Ethics
(FATE) of educational interventions supported by the use of Artificial Intelligence (AI) …

With the widespread use of Artificial Intelligence (AI), understanding the behavior of
intelligent agents and robots is crucial to guarantee successful human-agent collaboration …

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …