Can AI learn from any public code online?

A few days after GitHub released a new Copilot tool that generates complementary code for programmers’ projects, web developer Kyle Peacock Tweet The oddity he noticed.

“I love learning and building new things,” Algorithm wrote when asked to create an About Me page. “I have a Github account.”

My About page is presumably created for a fake person, but that link goes to David Celis’ GitHub profile. The Verge It is not the product of Copilot’s imagination. Celis is a coder and GitHub user with a reputable repository and previously worked for the company.

“I’m not surprised that my public repositories are part of Copilot’s training data,” Celis said. The Verge, added that he was delighted with the algorithm to recite his name. But while he doesn’t mind spitting his name out by algorithms that paraphrase training data, Celis is concerned about the copyright implications of GitHub, which scoops out any code it can find to improve AI.

When GitHub announced Copilot on June 29, the company said the algorithm was trained with publicly available code posted on GitHub. Nat Friedman, CEO of GitHub, has posted on forums like Hacker News. Twitter The company is legally clear. The Copilot page states, “Training machine learning models on publicly available data is considered fair use in the machine learning community.

However, the legal issues were not resolved, as Friedman said. The confusion goes beyond GitHub. Artificial intelligence algorithms work because of the vast amount of data they analyze, most of which comes from the open internet. An easy example is ImageNet, the most influential AI training dataset that consists entirely of publicly available images not owned by the ImageNet creators. If courts say it is not legal to use readily accessible data, it could make training AI systems much more expensive and less transparent.

Despite GitHub’s assertion, there is no direct legal precedent for keeping publicly available education data fair use in the US, Stanford Law School’s Mark Lemley and Bryan Casey published a paper on AI datasets and fair use last year. . Texas Law Review.

That doesn’t mean they’re against it. Lemley and Casey write that publicly available data should be considered fair use in order to improve algorithms and comply with the norms of the machine learning community.

And there are past examples to back up that opinion, they say. They think of the Google Books case where Google downloaded and indexed more than 20 million books to create a literary search database, analogous to learning algorithms. The Supreme Court upheld Google’s fair use argument on the grounds that the new tool would transform the original work and benefit readers and authors broadly.

“There’s no argument about the ability to store all copyrighted material in a computer-readable database,” Casey says of the Google Books case. “Then what the machine outputs is still blurry and you’ll figure it out.”

This means that the details change as the algorithm creates its own media. In their paper, Lemley and Casey argue that fair use assignments become much more ambiguous when algorithms start generating songs in the style of Ariana Grande or directly extracting new solutions from coders to problems.

Because this hasn’t been tested directly in court, judges aren’t forced to decide how much of the technology actually extracts. If an AI algorithm turns a copyrighted work into a profitable technology, the judge pays or credits the creators for what they take. It is the realm of possibility to decide what should be done.

But on the other hand, if a judge decides that GitHub’s teaching style for publicly available code is fair use, it would reduce the need for GitHub and OpenAI to cite the licenses of the coders who wrote the training data. For example, Celis, who created a GitHub profile at Copilot, says that he uses the Creative Commons Attribution 3.0 Unported License, which requires attribution for derivative works.

“And I fell into the camp of believing that Copilot’s generated code was absolutely derivative work,” he said. The Verge.

However, there is no definitive ruling as to whether this practice is legal until this is decided in court.

“I’m excited to see people using their code for education,” says Lemley. “It’s not necessarily going to show up in someone else’s work, but if we have a better-trained AI, we’ll all be better off.”


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