Human in the Loop

Human-in-the-loop (HITL) systems integrate human expertise with artificial intelligence to improve the performance, robustness, and ethical considerations of AI systems. Current research focuses on optimizing HITL interactions across diverse applications, including autonomous driving, medical image analysis, and robotics, often employing techniques like active learning, reinforcement learning, and large language models to guide human input and improve model efficiency. The significance of HITL lies in its ability to address limitations of fully automated systems, particularly in complex or ambiguous tasks, leading to more accurate, reliable, and ethically sound AI solutions across various scientific and practical domains.