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 …

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

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

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

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 …

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 …

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 …

Today, AI is being increasingly used to help human experts make decisions in high-stakes
scenarios. In these scenarios, full automation is often undesirable, not only due to the …

AI-assisted decision-making that impacts individuals raises critical questions about
transparency and fairness in artificial intelligence (AI). Much research has highlighted the …

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 …