Deep learning (DL) models have achieved impressive performance in various domains such
as medicine, finance, and autonomous vehicle systems with advances in computing power …

AI systems are adopted in numerous domains due to their increasingly strong predictive
performance. However, in high-stakes domains such as criminal justice and healthcare, full …

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

Many researchers motivate explainable AI with studies showing that human-AI team
performance on decision-making tasks improves when the AI explains its recommendations …

As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …

This paper contributes to the growing literature in empirical evaluation of explainable AI
(XAI) methods by presenting a comparison on the effects of a set of established XAI methods …

Abstract Human-Centered AI (HCAI) is a promising direction for designing AI systems that
support human self-efficacy, promote creativity, clarify responsibility, and facilitate social …

Understanding the recommendations of an artificial intelligence (AI) based assistant for
decision-making is especially important in high-risk tasks, such as deciding whether a …

The spread of AI-embedded systems involved in human decision making makes studying
human trust in these systems critical. However, empirically investigating trust is challenging …

In this paper, we describe a post-hoc explanation-by-example approach to eXplainable AI
(XAI), where a black-box, deep learning system is explained by reference to a more …