Label Bias Audit

Automatischer Uebersetzungsentwurf (German) for "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.

“Beispielentwurf: 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.”