Human-in-the-Loop (HITL)

Human-in-the-Loop (HITL) is an AI system design pattern that integrates human judgment and expertise into the training, validation, and operation processes of machine learning models. When an AI model has low confidence in its predictions or encounters situations requiring complex ethical judgment or common-sense reasoning, it can escalate issues to human experts for handling. Human feedback can then be used to improve the AI model, forming a continuous learning and optimization loop. HITL systems are particularly valuable in high-stakes applications like medical diagnosis, content moderation, and autonomous systems where human oversight can prevent costly errors and ensure ethical decision-making.