The emerging research field of Explainable Artificial Intelligence (XAI) addresses the
problem that users do not trust or blindly follow AI systems that act as black boxes. XAI …

With Artificial Intelligence (AI) becoming ubiquitous in every application domain, the need for
explanations is paramount to enhance transparency and trust among non-technical users …

Abstract Explainable Artificial Intelligence (XAI) is an aspiring research field addressing the
problem that users of AI do not trust AI systems that act as black boxes. However, XAI …

One of the major criticisms of Artificial Intelligence is its lack of explainability. A claim is made
by many critics that without knowing how an AI may derive a result or come to a given …

Human-Centered AI is a widely requested goal for AI applications. To reach this is
explainable AI promises to help humans to understand the inner workings and decisions of …

Explainable Artificial Intelligence (XAI) has re-emerged in response to the development of
modern AI and ML systems. These systems are complex and sometimes biased, but they …

The need for interpretable and accountable intelligent systems grows along with the
prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI …

Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of
Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to …

Explainability of AI systems is critical for users to take informed actions. Understanding who
opens the black-box of AI is just as important as opening it. We conduct a mixed-methods …

In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms,
providing a useful toolbox for researchers and practitioners to build XAI applications. With …