Human-in-the-Loop AI

Human-in-the-loop AI studies how people can teach, correct, guide, or oversee AI systems, from labeling examples in active learning to providing preferences in reinforcement learning and reviewing agent decisions. Common approaches include interactive interfaces, learning to defer uncertain cases to experts, and using human demonstrations or corrections to adapt robots and other systems during deployment. Applications range from medical decision support and education to robotics, scientific research, and software engineering, evaluated through benchmarks, user studies, and real-world trials. A central tension is how to make human input effective without creating excessive workload or encouraging overreliance, while preserving people’s ability to understand, challenge, and override AI decisions.