I think you’re describing introduction of noise to training sets, which is a staple of training. Your definitions of “fake” and “legitimate” are circular, and

I think you’re describing introduction of noise to training sets, which is a staple of training. Your definitions of “fake” and “legitimate” are circular, and miss the central point of large ML models: they can extrapolate from imperfect data because of the massive scale. Yes, the predictions will be imperfect. That’s true today, of both ML models and human radiologists. It’s about reducing the error rate, not designing a perfect algorithm that is never wrong. I’m pretty sure Gödel or someone can explain why the later isn’t even possible, for machine or human.