Artificial Intelligence systems are spreading to multiple applications and they are used by a
more diverse audience. With this change of the use scenario, AI users will increasingly …

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 …

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

As the use of AI algorithms keeps rising continuously, so does the need for their
transparency and accountability. However, literature often adopts a one-size-fits-all …

The increasing ubiquity of artificial intelligence (AI) has spurred the development of
explainable AI (XAI) to make AI more understandable. Even as novel algorithms for …

Over the last few years there has been rapid research growth into eXplainable Artificial
Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for …

Explainability (XAI) has matured in recent years to provide more human-centered
explanations of AI-based decision systems. While static explanations remain predominant …

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 …

The goal of Explainable AI (XAI) is to make the reasoning of a machine learning model
accessible to humans, such that users of an AI system can evaluate and judge the …

The rapid growth and use of artificial intelligence (AI)-based systems have raised concerns
regarding explainability. Recent studies have discussed the emerging demand for …