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.
Papers
Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop Annotations
Zhuowen Zhao, Xiaoya Chong, Tanny Chavez, Alexander Hexemer
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Yousef Emami, Luis Almeida, Kai Li, Wei Ni, Zhu Han
Operational Safety in Human-in-the-loop Human-in-the-plant Autonomous Systems
Ayan Banerjee, Aranyak Maity, Imane Lamrani, Sandeep K.S. Gupta