The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …

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 …

Large language models (LLMs) have demonstrated remarkable capabilities in natural
language understanding and generation across various domains, including medicine. We …

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 …

Prior work has identified a resilient phenomenon that threatens the performance of human-
AI decision-making teams: overreliance, when people agree with an AI, even when it is …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …

People supported by AI-powered decision support tools frequently overrely on the AI: they
accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the …

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 …

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 …