enh(blog): Add blog post on generative AI peer review policy

[lwasser]

This blog post outlines pyOpenSci's new peer review policy regarding the use of generative AI tools in scientific software, emphasizing transparency, ethical considerations, and the importance of human oversight in the review process.

It is codeveloped by the pyOpenSci community and relates to a discussion here:

pyOpenSci/software-peer-review#331

[lwasser]

@all-contributors please add @elliesch for review, blog

[allcontributors]

@lwasser

I've put up a pull request to add @elliesch! 🎉

[lwasser]

@all-contributors please add @elliesch for blog, review

[allcontributors]

@lwasser

@elliesch already contributed before to blog, review

[lwasser]

cc @willingc in case you are interested in this blog post!! no pressure!!

[willingc]

Love this! A few grammar suggestions.

[willingc]

Suggested change

	* Using LLM output verbatim could violate the original code's license
	* You might accidentally commit plagiarism or copyright infringement by using that output verbatim in your code
	* Due diligence is nearly impossible since you can't trace what the LLM "learned from" (most LLM's are black boxes)
	* Using LLM output verbatim could violate the original code's license
	* You might accidentally commit plagiarism or copyright infringement by using that output verbatim in your code
	* Due diligence is nearly impossible since you can't trace what the LLM "learned from" (most LLMs are black boxes)

[jedbrown]

I think "verbatim" is being leaned on too much here. An LLM can produce verbatim copies of its corpus, but the standard in copyright law is not limited to verbatim copies. If the process involved copying at any stage, refactoring can only obfuscate. The "substantial similarity" standards in copyright law are used as circumstantial evidence of process. Modifying the result by paraphrasing/refactoring is concealing the evidence (and thus reduces the likelihood of being caught), but does not make the process legal. I think we should be careful to not spread that misconception to readers.

[lwasser]

Suggested change

	LLMs are trained on large amounts of open source code; most of that code has licenses that require attribution.
	The problem? LLMs sometimes spit out near-exact copies of that training data, but without any attribution or copyright notices.
	LLMs are trained on large amounts of open source code that is bound by various licenses, many of which require attribution. When an LLM generates code, it may reproduce verbatim output or patterns or structures from that training data—but without attribution or copyright notices.

[lwasser]

Trying to include more than just verbatim ... that fundamentally, the patterns as well are licensed.

Also wondering here - let's say that i produce some code totally on my own that happens to match a pattern of some code with a license that requires attribution. What happens there? (if my production code is legitimately developed on my own and the pattern just happens to be a great one that others use too, and maybe I've even seen it before, but I'm not intentionally copying).

[jedbrown]

As far as copyright law is concerned, that's exactly the scenario where the substantial similarity standard would be applied. The more substantial the copying and the more closely in time that you would have observed the original, the more likely your work would be found to have substantial similarity and to be infringing. Protecting against that ambiguity is why clean-room design exists.

[lwasser]

Ok everyone!! I have addressed all of the comments now I believe!! It's time to merge this. Once merged, if you notice anything that still feels off please feel free to open a new pr or an issue here. I think after seeing the direction that JOSS is going we may also want to add a future discussion on reviewer burden, and communication in issues being human not agent based!

[lwasser]

@eliotwrobson merged!! 🚀