Human in the Loop refers to systems or processes where human intervention is actively used in combination with automated algorithms to improve outcomes. In machine learning, HITL is common in scenarios like active learning (where a model queries a human for labels on uncertain examples), interactive machine learning, or in monitoring model outputs to catch and correct errors (e.g., a human moderates outputs of an AI system to ensure quality). HITL acknowledges that fully autonomous AI isn’t always reliable or desirable, and human judgment can guide, correct, or augment automated processes. For example, in model training, a human might label data or adjust features; in deployment, a human might receive AI suggestions but make the final decision (common in medical diagnosis or loan approvals). HITL can significantly enhance system performance, especially in complex tasks that AI alone can’t solve perfectly, by leveraging human expertise and oversight.