Human-in-the-Loop

Human-in-the-Loop (HITL) is a methodology in artificial intelligence and machine learning where human feedback is integrated into the training and decision-making processes of AI systems. This approach enhances model accuracy by allowing human operators to provide insights, corrections, and contextual understanding that machines may lack. HITL is particularly useful in complex tasks where nuanced judgment is required, such as in natural language processing, computer vision, and healthcare diagnostics. By involving humans, systems can continually improve through iterative learning, adapting to new data and changing environments more effectively.