Label Label Review

Label Label Review is a ml quality workflow that checks annotations for consistency and usefulness for ground-truth or weak-supervision annotation. It uses agreement metrics, reviewer queues, and adjudication so teams can improve supervised learning data while keeping evidence, reliability, and public-safe operational boundaries clear.

“The machine learning team used Label Label Review when the label set had disagreement, so the team could improve supervised learning data before the model moved into evaluation.”