Its not hypocritical to follow a line of legal analysis whoch holds that copying material in the course of training AI on it is outside the scope of copyright protection (as, e.g., fair use in the US), but that the model weights resulting from the training are protected by copyright.
It maybe wrong, and it may be convenient for the interests of the firms involved, but it is not self-inconsistent in the way required for it to be hypocrisy.
Educated human beings are not protected by copyright, hence neither should trained AI models. Conversely, if a copyrightable work is produced based on work which itself is copyrighted, the resulting work needs the consent of the original authors of the prior work.
AI models can't have their ©ake and eat it.
No one training (foundation) models makes that fair use argument by analogy, they make arguments that addresses the specific statutory and case law criteria for fair use (abd frequently focus on the transformative character of the use); its true that the analogy to a learning human argument is frequently made in internet fora by AI enthusiasts who aren't the people training models on vaat scraped datasets. That argument is bunk for a number of reasons, but most critically the fact that a human learning from material isn’t fair use, because a human brain isn’t treated as a fixed medium, so learning in a human brain isn’t legally a copy or derivative work that would violate copyright without the fair use exception, so it's not a use to which fair use analysis even applies, so you can't argue anything is fair use by analogy to that. But its moot to any argument for hypocrisy by the big model makers, because they aren’t using that argument to start with.
(Such a model/statistical-summary, along with a dictionary, could be used to generate nonsensical texts which have similar patterns in terms of just word lengths.)
Should the resulting work be protected by copyright? I’m not entirely sure…
I guess one thing is, the specific numbers I obtain by doing this are not a consequence of any creative decision making on my part, which I think in some jurisdictions (I don’t remember which) plays a role in whether a work is copyrightable (I will use “copyrightable” as an abbreviation for “protected by copyright”. I don’t mean to imply a requirement that someone specifically registers for copyright.). (Iirc this makes it so phone books are copyrightable in some jurisdictions but not others?)
The particular choice of statistical analysis does seem like it may involve creative decision making, but that would just be about like, what analysis I describe, and how the numbers I publish are to be interpreted, not what the numbers are? (Analogous to the source code of an ML model, not the parameters.)
Here is another question: suppose there is a method of producing a data artifact which would be genuinely (and economically) useful, and which does not rely on taking in any copyrighted input, but requires a large (expensive) amount of compute to produce, and which also uses a lot of randomness so that the result would be different each time it was done (but suppose also that there isn’t much point doing it multiple times at the same scale, as having two of this kind of data artifact wouldn’t be much more valuable than having one).
Should such data artifacts be protected by copyright or something like it?
Well, if copyright requires creative human decision making, then they wouldn’t be.
It seems like it would make sense to want it to be economically incentivized to create such data artifacts of higher sizes (to a point of course. Only as much as is justified by the value that is produced by them being available.) .
If such data artifacts can always be distributed without restriction, then ones that are publicly available would be public goods, and I guess only ones that are trade secrets would be private goods? It seems to me like having some mechanism to incentivize their creation and being-eventually-freely-distributed would be beneficial?
But maybe copyright isn’t the best way to do that? Idk.
The selection and structuring of the training set may involve sufficient creativity to be copyrightable (as demonstrated by the existence of “compilation copyrights”), even if it is largely or even entirely composed of existing works, the statistical analysis part doesn't have to be the source of the creativity.
This has already been settled hasn't it? Don't companies have to introduce 'flaws' in order for data sets to be 'protected'? Just compiled lists of facts can't be protected. Which is why things like election result companies having to rely on NDAs and not copyright protections to protect their services on election night.
No, flaws are generally introduced to make it easier to detect copies; if multiple flawless reference works covering the same data (road maps of the same region, for instance) exist, each is copyrightable without flaws to the extent any would be with flaws, but you can't prove that someone else copied yours without permission if copying any of the others would give the same result. With flaws, gou can attribute the source that was copied more easily, but this isn't about being legally protected but about the practicality of enforcing that protection.
Exactly. It would be patents, not copyright.
In my unusually well-informed on copyright but not a lawyer opinion, without any new legislation on the subject, I suspect that the most likely scenario for intellectual property rights surrounding AI is that using other people's works for training probably falls under fair use, since it's extremely transformative (an AI that makes text and a textual work are very different things) and it's extremely difficult to argue that the AI, as it exists today, directly impacts the value of the original work.
The list of what traing data to use is probably protected by copyright if hand-picked, otherwise just whatever web-crawler they wrote to gather it.
The AI models, as in, the inference and training applications are protected by copyright, like any other application.
The architecture of a particular AI model can be protected by patents.
The weights, as the result of an automated process, are probably not protected by copyright.
Object code is the result of an automated process and is covered by the copyright on the source code.
Compilations are covered by copyright separate from that of the individual works, and it is arguable that a training set would be covered by a compilation copyright, and the result of applying an automated training processs to it would remain covered by that copyright.
To substitute either party with a computer system and assume that the existing law still makes sense may be assuming too much.
