Human-in-the-Loop

Human-in-the-loop refers to AI system designs that incorporate human judgment, feedback, or oversight at critical stages of the machine learning lifecycle, from data labeling and model training to deployment monitoring and decision validation. This approach is essential for tasks where full automation carries significant risk, such as medical diagnosis, legal analysis, and content moderation for hate speech detection. In training pipelines, human annotators provide ground truth labels and evaluate model outputs through techniques like reinforcement learning from human feedback. During inference, human reviewers can verify model predictions before they are acted upon, adding a safety layer that is central to responsible AI and trustworthy AI frameworks. Human-in-the-loop systems strike a balance between the efficiency of automation and the nuanced judgment that humans provide, particularly in ambiguous or high-stakes scenarios.