So it is the opposite of attribution. It just makes up a plausible-looking story of attribution and passes it off as the truth. If you passed it a hand-painted image from 1850 which was not in SD's training dataset, it can happily declare that it was inspired by someone's piece from 2019. There's no actual causal inference going on.

It has more in common with using an AI language model as a "bullshit generator" than it has with human attribution. Which isn't a coincidence, since nearest neighbour similarity searches ARE a type of machine learning, just a much simpler one than SD.

Anti AI people who are upset about attribution, should learn the technology and try to create an actual attribution model: one that has a notion of causality, which could say who influenced who.

Causal model requires causal assumptions, but you can probably get far with the simple assumption that "works from the future do not influence works from the past".