Japan’s government will not enforce copyrights on data used in AI training

> If those people need to already have a copy of the image to extract a copy of the image from the generative image model, then I'm saying the argument that the model itself is infringing seems weak.

AFAIK, they don't need that to extract it.

> Instead, you already have to have a copy of an image to find an embedding.

You literally always need a copy or a suitable imprecise hash of the original to test for infringement. How else would you know which copyright was infringed? But it's not a matter of a 1-1 match (see below).

> However, it's a case-by-case thing.

Of course, it's a case-by-case thing, as the OP also indicated. It's easy to show mathematically that these models cannot compress well enough to contain all training images. The question is how much they infringe on some of them.

Bear in mind that it is not necessary at all to create a perfect copy of an image the infringe copyright. As I've stated above, even a lousy and mostly incorrect rendition of a pop song in a street cafe may infringe copyright. The makers behind the song "Blurred Lines" lost a lawsuit because the cowbell rhythm in the background was similar to that of another song. That and the "feeling" was similar.

The same is true for images. What counts are criteria like artistic originality, intent, subjective similarities, experts laying out similarities in style, and so on.

I mean, don't get me wrong, I understand perfectly well what you're trying to argue for. All I'm saying is that it doesn't match the reality of how the law deals with copyright.

> The issue is if they show up as part of some meme chat and then you as the LLM creator get sued because people start harassing them.

This seems a more obscure concern than extraction of data.

> copying is a basic part of how these algorithms work

Do you mean during training/gradient descent, or reverse diffusion?

No, you're over-generalizing what I'm saying (a.k.a. straw man fallacy).

If you're commercializing e.g. goodies that use the exact same character designed by some artist, so that anybody can tell you it's the same, AND if the traits of this character are actually original (not just so that anybody would come up with it independently), AND if the people buying your goodies are all thinking about the original character in the first place, AND if the original author is alive, then I'd absolutely agree that royalties need to be paid.

That's just an example. To tell you that in specific cases it's obvious that royalties are required.

But in the general case, no. Because, else, anything just is a derived work. Just think about it.

Are you sure? If you look at plagiarism in music, you'll find a number of cases where the defendant makes a compelling point about not remembering or consciously knowing they heard the original song before. For legal purposes, it is not the point, but they feel morally wronged to be charged as guilty. The case here is that they internalized the music knowledge, but forgot about the source - so they can't make the distinction you claim anymore. Natural selection shaped our brains to store formation that seems useful, not is attribution.

LLMs are also not usually trained to remenber where the examples they were trained on came from, the sourcing information is often not even there (maybe they could, maybe they should, but they aren't). Given that and the way training works, one could argue that they're never copying, only re-expressing or combining (which I think of as a form of "generating something original"). Just memorizing and copying is overfitting, and strongly undesirable, as it's not usable outside of the exact source context. I agree it can happen, but it's a flaw in the training process. I'd also agree that any instance of exact reproductions (or of material with similarity to the original content over some high threshold) is indeed copyright infringement, punishable as such.

So, my point is, training a model on copyrighted material is legal, but letting that model output copies of copyrighted material beyond fair use (quotations, references, etc - that make sense in the context the model was queried on) is an infringement. And since the actual training data is not necessarily known, providers of model-as-a-service, such as OpenAI with GPT, should be responsible for that.

In cases where a model was made available to others, it falls on the user of the model. If the training data is available, they should check answers against it (there's a whole discussion on how training data should be published to support this) to avoid the risk;if the training data is unknown, they're taking the risk of being sued full-on, without any mitigation.

The interesting thing to me is determining when something that is acceptable for humans to do at human scale is also acceptable for machines to do at industrial scale.

There are many examples. One is face recognition - clearly it is acceptable for individual humans to do this at small scale, but systematic identification of everyone on a street or in a stadium has different implications for society.

(In this case it almost doesn't matter whether the surveillance is performed by humans or machines - it's the scale and systematic nature that changes the equation.)