> David is disclosing it in the maillist and the GH page. Should the disclosure > be persisted in the commit?
Someone asked me to update the ML, but I don’t believe or agree with us assuming we should do this for every PR; personally storing this in the PR description is fine to me as you are telling the reviewers (who you need to communicate this to). > I’d say we can use the co-authored part of our commit messages to disclose > the actual AI that was used? Heh... I kinda feel dirty doing that… No one does that when they take something from a blog or stack overflow, but when you do that you should still attribute by linking… which I guess is what Co-Authored does? I don’t know… feels dirty... > On Jul 23, 2025, at 11:19 AM, Bernardo Botella <conta...@bernardobotella.com> > wrote: > > That’s a great point. I’d say we can use the co-authored part of our commit > messages to disclose the actual AI that was used? > > > >> On Jul 23, 2025, at 10:57 AM, Yifan Cai <yc25c...@gmail.com> wrote: >> >> Curious, what are the good ways to disclose the information? >> >> > All of which comes back to: if people disclose if they used AI, what >> > models, and whether they used the code or text the model wrote verbatim or >> > used it as a scaffolding and then heavily modified everything I think >> > we'll be in a pretty good spot. >> >> David is disclosing it in the maillist and the GH page. Should the >> disclosure be persisted in the commit? >> >> - Yifan >> >> On Wed, Jul 23, 2025 at 8:47 AM David Capwell <dcapw...@apple.com >> <mailto:dcapw...@apple.com>> wrote: >>> Sent out this patch that was written 100% by Claude: >>> https://github.com/apache/cassandra/pull/4266 >>> >>> Claudes license doesn’t have issues with the current ASF policy as far as I >>> can tell. If you look at the patch it’s very clear there isn’t any >>> copywriter material (its glueing together C* classes). >>> >>> I could have written this my self but I had to focus on code reviews and >>> also needed this patch out, so asked Claude to write it for me so I could >>> focus on reviews. I have reviewed it myself and it’s basically the same >>> code I would have written (notice how small and focused the patch is, >>> larger stuff doesn’t normally pass my peer review). >>> >>>> On Jun 25, 2025, at 2:37 PM, David Capwell <dcapw...@apple.com >>>> <mailto:dcapw...@apple.com>> wrote: >>>> >>>> +1 to what Josh said >>>> Sent from my iPhone >>>> >>>>> On Jun 25, 2025, at 1:18 PM, Josh McKenzie <jmcken...@apache.org >>>>> <mailto:jmcken...@apache.org>> wrote: >>>>> >>>>> >>>>> Did some more digging. Apparently the way a lot of headline-grabbers have >>>>> been making models reproduce code verbatim is to prompt them with dozens >>>>> of verbatim tokens of copyrighted code as input where completion is then >>>>> very heavily weighted to regurgitate the initial implementation. Which >>>>> makes sense; if you copy/paste 100 lines of copyrighted code, the >>>>> statistically likely completion for that will be that initial >>>>> implementation. >>>>> >>>>> For local LLM's, the likelihood of verbatim reproduction is differently >>>>> but apparently comparably unlikely because they have far fewer parameters >>>>> (32B vs. 671B for Deepseek for instance) of their pre-training corpus of >>>>> trillions (30T in the case of Qwen3-32B for instance), so the individual >>>>> tokens from the copyrighted material are highly unlikely to be actually >>>>> stored in the model to be reproduced, and certainly not in sequence. They >>>>> don't have the post-generation checks claimed by the SOTA models, but are >>>>> apparently considered in the "< 1 in 10,000 completions will generate >>>>> copyrighted code" territory. >>>>> >>>>> When asked a human language prompt, or a multi-agent pipelined "still >>>>> human language but from your architect agent" prompt, the likelihood of >>>>> producing a string of copyrighted code in that manner is statistically >>>>> very, very low. I think we're at far more risk of contributors >>>>> copy/pasting stack overflow or code from other projects than we are from >>>>> modern genAI models producing blocks of copyrighted code. >>>>> >>>>> All of which comes back to: if people disclose if they used AI, what >>>>> models, and whether they used the code or text the model wrote verbatim >>>>> or used it as a scaffolding and then heavily modified everything I think >>>>> we'll be in a pretty good spot. >>>>> >>>>> On Wed, Jun 25, 2025, at 12:47 PM, David Capwell wrote: >>>>>> >>>>>>> 2. Models that do not do output filtering to restrict the reproduction >>>>>>> of training data unless the tool can ensure the output is license >>>>>>> compatible? >>>>>>> >>>>>>> 2 would basically prohibit locally run models. >>>>>> >>>>>> >>>>>> I am not for this for the reasons listed above. There isn’t a difference >>>>>> between this and a contributor copying code and sending our way. We >>>>>> still need to validate the code can be accepted . >>>>>> >>>>>> We also have the issue of having this be a broad stroke. If the user >>>>>> asked a model to write a test for the code the human wrote, we reject >>>>>> the contribution as they used a local model? This poses very little >>>>>> copywriting risk yet our policy would now reject >>>>>> >>>>>> Sent from my iPhone >>>>>> >>>>>>> On Jun 25, 2025, at 9:10 AM, Ariel Weisberg <ar...@weisberg.ws >>>>>>> <mailto:ar...@weisberg.ws>> wrote: >>>>>>> 2. Models that do not do output filtering to restrict the reproduction >>>>>>> of training data unless the tool can ensure the output is license >>>>>>> compatible? >>>>>>> >>>>>>> 2 would basically prohibit locally run models. >>>>> >>> >
