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 …

Explainability is an essential pillar of Responsible AI that calls for equitable and ethical
Human-AI interaction. Explanations are essential to hold AI systems and their producers …

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 …

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 …

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 …

As AI-powered systems increasingly mediate consequential decision-making, their
explainability is critical for end-users to take informed and accountable actions. Explanations …

Explanations—a form of post-hoc interpretability—play an instrumental role in making
systems accessible as AI continues to proliferate complex and sensitive sociotechnical …

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 …

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 …

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 …