GitHub’s Automatic Coding Tool Rests on Untested Legal Ground
DannyB writes:
GitHub's automatic coding tool rests on untested legal ground:
The Copilot tool has been trained on mountains of publicly available code
[...] When GitHub announced Copilot on June 29, the company said that the algorithm had been trained on publicly available code posted to GitHub. Nat Friedman, GitHub's CEO, has written on forums like Hacker News and Twitter that the company is legally in the clear. Training machine learning models on publicly available data is considered fair use across the machine learning community," the Copilot page says.
But the legal question isn't as settled as Friedman makes it sound - and the confusion reaches far beyond just GitHub. Artificial intelligence algorithms only function due to massive amounts of data they analyze, and much of that data comes from the open internet. An easy example would be ImageNet, perhaps the most influential AI training dataset, which is entirely made up of publicly available images that ImageNet creators do not own. If a court were to say that using this easily accessible data isn't legal, it could make training AI systems vastly more expensive and less transparent.
Despite GitHub's assertion, there is no direct legal precedent in the US that upholds publicly available training data as fair use, according to Mark Lemley and Bryan Casey of Stanford Law School, who published a paper last year about AI datasets and fair use in the Texas Law Review.
[...] And there are past cases to support that opinion, they say. They consider the Google Books case, in which Google downloaded and indexed more than 20 million books to create a literary search database, to be similar to training an algorithm. The Supreme Court upheld Google's fair use claim, on the grounds that the new tool was transformative of the original work and broadly beneficial to readers and authors.
Microsoft's GitHub Copilot Met with Backlash from Open Source Copyright Advocates:
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