Human-in-the-Loop AI
Human-in-the-Loop AI is an approach where human intelligence is integrated into the machine learning lifecycle to improve model performance, accuracy, and reliability. It involves humans providing feedback, labeling data, or making decisions that guide AI systems, particularly in tasks where automation alone is insufficient or error-prone. This methodology is commonly used in areas like data annotation, model validation, and adaptive learning systems.
Developers should learn Human-in-the-Loop AI when building AI applications that require high accuracy, handle ambiguous or complex data, or need continuous improvement through user feedback. It is essential for use cases such as medical diagnosis, content moderation, autonomous vehicles, and customer service chatbots, where human oversight can correct errors, reduce bias, and enhance trust in AI systems.