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 …

Explainability of AI systems is crucial to hold them accountable because they are
increasingly becoming consequential in our lives by powering high-stakes decisions in …

The realm of Artificial Intelligence (AI)'s impact on our lives is far reaching–with AI systems
proliferating high-stakes domains such as healthcare, finance, mobility, law, etc., these …

Recent years have seen a surge of interest in the field of explainable AI (XAI), with a
plethora of algorithms proposed in the literature. However, a lack of consensus on how to …

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on
the topic. While many recognize the necessity to incorporate explainability features in AI …

Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the
sociotechnical gap-divide between the technical affordances and the social needs …

Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …

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 …

A pervasive design issue of AI systems is their explainability--how to provide appropriate
information to help users understand the AI. The technical field of explainable AI (XAI) has …

Explainability is a hot topic nowadays for artificial intelligent (AI) systems. The role of
machine learning (ML) models on influencing human decisions shed light on the back-box …