Machine-learning prediction methods have been extremely productive in applications
ranging from medicine to allocating fire and health inspectors in cities. However, there are a …

The increasing availability of electronic health data presents a major opportunity in
healthcare for both discovery and practical applications to improve healthcare. However, for …

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

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 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 …

We analyze how advice from an AI affects complementarities between humans and AI, in
particular what humans know that an AI does not know:“unique human knowledge.” In a …

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

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 …

Although decision‐making algorithms are not new to medicine, the availability of vast stores
of medical data, gains in computing power, and breakthroughs in machine learning are …

Abstract Machine learning has made rapid advances in safety-critical applications, such as
traffic control, finance, and healthcare. With the criticality of decisions they support and the …