Label Bias Audit

Label Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for ground-truth or weak-supervision annotation. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.

“The machine learning team used Label Bias Audit when the label set had disagreement, so the team could surface fairness risks before the model moved into evaluation.”