Although AI holds promise for improving human decision making in societally critical
domains, it remains an open question how human-AI teams can reliably outperform AI alone …

Several strands of research have aimed to bridge the gap between artificial intelligence (AI)
and human decision-makers in AI-assisted decision-making, where humans are the …

AI explanations are often mentioned as a way to improve human-AI decision-making, but
empirical studies have not found consistent evidence of explanations' effectiveness and, on …

While a vast collection of explainable AI (XAI) algorithms has been developed in recent
years, they have been criticized for significant gaps with how humans produce and consume …

In many practical applications of AI, an AI model is used as a decision aid for human users.
The AI provides advice that a human (sometimes) incorporates into their decision-making …

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 …

Decisions made by human-AI teams (eg., AI-advised humans) are increasingly common in
high-stakes domains such as healthcare, criminal justice, and finance. Achieving high team …

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

Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to
enable humans to gain insight into the decision-making of machine learning models …

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 …