Dataset Bias Audit

Dataset Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for labeled and unlabeled data used for learning. 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 Dataset Bias Audit when the dataset received a new batch, so the team could surface fairness risks before the model moved into evaluation.”