STATERA Volume 41 Chapter 9 Explores Benchmarks for Human-in-the-Loop AI Learning

The chapter presents a framework for benchmarking artificial intelligence systems that integrate human judgment, feedback, correction, and supervision into learning and decision-making. Unlike conventional benchmarks that measure model accuracy in isolation, these HITL benchmarks assess how human involvement affects learning efficiency, reliability, adaptability, operational cost, and overall system performance. The design evaluates three components: the AI model, the human participant, and the interaction mechanism connecting them. Specific experimental setups are discussed for active learning, human feedback, preference learning, and reinforcement learning from human or AI feedback, with metrics such as learning curves and agreement between evaluators.