Analyzing the legal implications of GitHub Copilot

No thought experiment needed. If I watch a bunch of movies and then make my own movie, whether or not I lose in court depends on if the movie I made is at least "substantially similar" to any movie I happened to watch - or, in other words, had "access" to. That's a fact-intensive thing that juries usually decide on a case-by-case basis.

The difference between that and having an AI do it is probably low. My gut instinct is that using an AI constitutes "access" to the AI's training corpus, so if it spits out something at least substantially similar to that corpus, then I'm infringing if I use that output. If it doesn't constitute access, then a copyright owner would have to prove "striking similarity", which would really only cover things like using Copilot to spit out fragments of old Quake code verbatim.

Clean-room is a way of arguing down the level of access that you have to something that you want to make a non-infringing copy of. It usually requires having actual attorneys review everything the clean-room engineers get to see, and stripping out the parts that are actually copyrightable. Merely training an ML system on input as a way to only have access to the uncopyrightable parts of that input probably wouldn't work.

Pretty much every Internet service is going to have similar clauses to GitHub's; because anything else would basically be a "click here to make me liable for copyright infringement" button. In fact, I wouldn't be surprised that merely running something like GitHub but without a ToS would still give you similar levels of implied license over whatever people push to your server.

I'm thinking more "association" than "learning", and in both cases.

If an algorithm of some sort scans a bunch of repos regarding video encoding and decoding and sees a lot of ffmpeg use, it might associate ffmpeg with video encoding and decoding, and decide to present some info about ffmpeg and a generic snippet to include ffmpeg as a library and initialize it if it associates the current project with that.

If I have perused a few encoding or decoding repos at some point and I think of the current project as having to do with encoding or decoding of video, I might immediately think ffmpeg even if I've never used it in a project as a library because I remembered seeing it in projects that used it, and look for some initialization code.

In what ways are these materially different? What makes the random conceptual associations in my head from what I've seen previously different than an algorithm that collects the same?

> Training models is more analogous to compilation or lossy encoding or compression.

And learning in people isn't? Isn't all knowledge transference in people analogous to lossy encoding and compression?

I don't know about you, but in college I don't remember regurgitating sections of "Advanced Programming in the UNIX Environment" to complete assignments, I remember studying it, internalizing parts of it on a conceptual level (as well as remembering specific fairly small chunks almost exactly), and using that to solve problems or answer questions or make associations.

I'm not saying ML and and learning in humans is the same. I do think for the very specific case presented here in how it's used, there are some parallels. Feel free to disabuse me of that notion if you have evidence that contradicts it though. I'm not wedded to that position, but I would want to see arguments to the contrary before abandoning it.

I think the discussions miss a bit an important point. IANAL, but I think if a young programmer reads a lot of source code on GitHub, and based on this reading becomes a better programmer, this is a fair use of copyrighted material and pretty much independant of the license. If I read any book and learn the corresponding language, this isn't a copyright violation of the book either. This starts, when I begin to quote from that book or the programmer takes snippets from the programs that got read.

The problem is, I don't think you can really claim that Copilot learned to program. While some of the output seems to be something new, most of the times it looks more like a recomposition of learned fragments if not even longer pieces of verbatim code taken from copyrighted material. We have seen examples of this. And in this moment, it becomes a copyright discussion, probably determined by the volume of copyrighted material reproduced. Which by the way is always the risk if a human uses certain training material. The better one is at memorizing things, the more there is the risk.

Or put it the other way around: if Copilot would use its "knowledge" of programs to advise the programmer like pointing out potential errors without reproducing anything it used for learning, it should be fine. But that is not how it works.

> So with respect to code that’s already on GitHub, I think the answer to the question of copyright infringement is fairly straightforward.

That statement is wrong.

Github does not know if the uploader of some lines of code is actually the author of said code or otherwise allowed to upload it.

Imagine I uploaded the source code of Microsoft Windows to Github.

The Github ToS can force users to grant them whatever they want, but I am legally unable to grant them any usage rights for the Microsoft Windows code that I uploaded.

There will be many cases of people uploading code, including open source code, from elsewhere to Github, and the original author not caring or whatever (e.g. informal mirrors), where the uploader is unable to grant Github any rights.