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Re: Proposal -- Interpretation of DFSG on Artificial Intelligence (AI) Models



On 28.04.25 00:02, Russ Allbery wrote:
Some of
them, however, will be trained on things that are widely recognized to be
non-copyrightable facts, such as records of backgammon, chess, or go
games.
However, the manifested collection of such games might be covered by some database aggregate copyright-or-something-like-it law, which doesn't help.
However, even that is tricky, because the*annotations* on chess games can
be copyrighted.

… which is only a problem if the chess engine considers them for training, which I assume they don't, so we could conceivable DFSG-ize the data.

The fact remains that our builders will be unable to reproduce the resulting network, for well-known practical reasons. Thus we mostly-have-to-trust the original publisher that their network has been built as documented (or even "documented" given the status of gnubg). In practice this is not a problem for a Backgammon engine, or even for Tesseract because any serious use case supports, if not requires, human verification of the result — but how sure can I be that a LLM intended for home automation doesn't contain an Open Sesame backdoor that unlocks my *home*'s back door?

--
-- regards
--
-- Matthias Urlichs

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