Human-in-the-Loop
Inserting human review into an AI workflow for oversight on important decisions.
Human-in-the-loop (HITL) is a design pattern that routes AI outputs to a human reviewer before or after the model acts, ensuring accountability where errors carry real consequences. On AWS, Amazon Augmented AI (A2I) operationalizes this by letting builders define review workflows for services such as Amazon Rekognition (image moderation) and Amazon Textract (document extraction), routing low-confidence predictions to a private workforce, Amazon Mechanical Turk, or a vendor workforce. The key exam distinction is that HITL trades throughput for safety, making it suited to regulated or high-stakes decisions rather than high-volume commodity inference where human review would be cost-prohibitive.
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