User in the Loop

"User-in-the-loop" (UitL) systems integrate human oversight and interaction into automated processes, aiming to improve performance, reliability, and ethical considerations. Current research focuses on optimizing human-machine collaboration in diverse applications, from robotic manipulation and object retrieval (employing learning-to-rank and actor-critic models) to knowledge graph generation and algorithmic decision-making in high-stakes domains. This approach is crucial for addressing challenges in complex tasks where human expertise and judgment remain essential, leading to more robust, adaptable, and ethically sound systems across various fields.