AI tooling must be disclosed for contributions

Provenance matters. An LLM cannot certify a Developer Certificate of Origin (https://en.wikipedia.org/wiki/Developer_Certificate_of_Origi...) and a developer of integrity cannot certify the DCO for code emitted by an LLM, certainly not an LLM trained on code of unknown provenance. It is well-known that LLMs sometimes produce verbatim or near-verbatim copies of their training data, most of which cannot be used without attribution (and may have more onerous license requirements). It is also well-known that they don't "understand" semantics: they never make changes for the right reason.

We don't yet know how courts will rule on cases like Does v Github (https://githubcopilotlitigation.com/case-updates.html). LLM-based systems are not even capable of practicing clean-room design (https://en.wikipedia.org/wiki/Clean_room_design). For a maintainer to accept code generated by an LLM is to put the entire community at risk, as well as to endorse a power structure that mocks consent.

For a large LLM I think the science in the end will demonstrate that verbatim reproduction is not coming from verbatim recording, as the structure really isn’t setup that way in the models under question here.

This is similar to the ruling by Alsup in the Anthropic books case that the training is “exceedingly transformative”. I would expect a reinterpretation or disagreement on this front from another case to be both problematic and likely eventually overturned.

I don’t actually think provenance is a problem on the axis you suggest if Alsups ruling holds. That said I don’t think that’s the only copyright issue afoot - the copyright office writing on copyrightability of outputs from the machine essentially requires that the output fails the Feist tests for human copyrightability.

More interesting to me is how this might realign the notion of copyrightability of human works further as time goes on, moving from every trivial derivative bit of trash potentially being copyrightable to some stronger notion of, to follow the feist test, independence and creativity. Further it raises a fairly immediate question in an open source setting if many individual small patch contributions themselves actually even pass those tests - they may well not, although the general guidance is to set the bar low - but is a typo fix either? There is so far to go on this rabbit hole.

So if you can get an LLM to produce music lyrics, for example, or sections from a book, those would be considered novel works given the encoding as well?

"an LLM" could imply an LLM of any size, for sufficiently small or focused training sets an LLM may not be transformative. There is some scale at which the volume and diversity of training data and intricacy of abstraction moves away from something you could reasonably consider solely memorization - there's a separate issue of reproduction though.

"novel" here depends on what you mean. Could an LLM produce output that is unique that both it and no one else has seen before, possibly yes. Could that output have perceived or emotional value to people, sure. Related challenge: Is a random encryption key generated by a csprng novel?

In the case of the US copyright office, if there wasn't sufficient human involvement in the production then the output is not copyrightable and how "novel" it is does not matter - but that doesn't necessarily impact a prior production by a human that is (whether a copy or not). Novel also only matters in a subset of the many fractured areas of copyright laws affecting the space of this form of digital replication. The copyright office wrote: https://www.copyright.gov/ai/Copyright-and-Artificial-Intell....

Where I imagine this approximately ends up is some set of tests that are oriented around how relevant to the whole the "copy" is, that is, it may not matter whether the method of production involved "copying", but may more matter if the whole works in which it is included are at large a copy, or, if the area contested as a copy, if it could be replaced with something novel, and it is a small enough piece of the whole, then it may not be able to meet some bar of material value to the whole to be relevant - that there is no harmful infringement, or similarly could cross into some notion of fair use.

I don't see much sanity in a world where small snippets become an issue. I think if models were regularly producing thousands of tokens of exactly duplicate content that's probably an issue.

I've not seen evidence of the latter outside of research that very deliberately performs active search for high probability cases (such as building suffix tree indices over training sets then searching for outputs based on guidance from the index). That's very different from arbitrary work prompts doing the same, and the models have various defensive trainings and wrappings attempting to further minimize reproductive behavior. On the one hand you have research metrics like 3.6 bits per parameter of recoverable input, on the other hand that represents a very small slice of the training set, and many such reproductions requiring strongly crafted and long prompts - meaning that for arbitrary real world interaction the chance of large scale overlap is small.