Human-in-the-Loop (HITL)

Human-in-the-loop (HITL) is a framework that integrates human judgment into the machine learning lifecycle to improve training, validation, and output quality.

In a HITL workflow, humans perform initial labeling, review and correct model predictions, and handle low-confidence edge cases.

HITL is often used for active learning, model monitoring, and quality control. Corrected outputs are fed back into the training set to create an iterative data feedback loop.

In high-stakes domains such as healthcare and law, HITL is a key control for reliability.