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 …

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 …

Intelligent agents must be able to communicate intentions and explain their decision-making
processes to build trust, foster confidence, and improve human-agent team dynamics …

Recent advances in artificial intelligence (AI) have drawn attention to the need for AI systems
to be understandable to human users. The explainable AI (XAI) literature aims to enhance …

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 …

Introduction With the advancement of technology and the increasing utilization of AI, the
nature of human work is evolving, requiring individuals to collaborate not only with other …

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 …

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 …

The dazzling promises of AI systems to augment humans in various tasks hinge on whether
humans can appropriately rely on them. Recent research has shown that appropriate …

Deep reinforcement learning has generated superhuman AI in competitive games such as
Go and StarCraft. Can similar learning techniques create a superior AI teammate for human …