Every labeling process produces errors. The question is not whether your training data has mislabeled examples, it does, but whether you catch them before they teach your model the wrong thing. This is what annotation auditing does: a human-in-the-loop review layer that finds and fixes labeling errors, whether the labels came from your own team, […]