Experiment Bias Audit

機械支援の翻訳下書き (Japanese) for "Experiment Bias Audit": Experiment Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for controlled model comparison. 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.

“例文の下書き: The machine learning team used Experiment Bias Audit when the experiment showed a metric tradeoff, so the team could surface fairness risks before the model moved into evaluation.”