In addition to the trove of books, the Institutional Data Initiative is also working with the Boston Public Library to scan millions of articles from different newspapers now in the public domain, and it says it’s open to forming similar collaborations down the line. The exact way the books dataset will be released is not settled. The Institutional Data Initiative has asked Google to work together on public distribution, and the company has pledged its support.
However the IDI’s dataset is released, it will be joining a host of similar projects, startups, and initiatives that promise to give companies access to substantial and high-quality AI training materials without the risk of running into copyright issues. Firms like Calliope Networks and ProRata have emerged to issue licenses and design compensation schemes designed to get creators and rightsholders paid for providing AI training data.
There are also other new public-domain projects. Last spring, the French AI startup Pleias rolled out its own public-domain dataset, Common Corpus, which contains an estimated 3 to 4 million books and periodical collections, according to project coordinator Pierre-Carl Langlais. Backed by the French Ministry of Culture, the Common Corpus has been downloaded more than 60,000 times this month alone on the open source AI platform Hugging Face. Last week, Pleias announced that it is releasing its first set of large language models trained on this dataset, which Langlais told WIRED constitute the first models “ever trained exclusively on open data and compliant with the [EU] AI Act.”
Efforts are underway to create similar mage datasets as well. AI startup Spawning released its own this summer called Source.Plus, which contains public-domain images from Wikimedia Commons as well as a variety of museums and archives. Several significant cultural institutions have long made their own archives accessible to the public as standalone projects, like the Metropolitan Museum of Art.
Ed Newton-Rex, a former executive at Stability AI who now runs a nonprofit that certifies ethically-trained AI tools, says the rise of these datasets shows that there’s no need to steal copyrighted materials to build high-performing and quality AI models. OpenAI previously told lawmakers in the United Kingdom that it would be “impossible” to create products like ChatGPT without using copyrighted works. “Large public domain datasets like these further demolish the ‘necessity defense’ some AI companies use to justify scraping copyrighted work to train their models,” Newton-Rex says.
But he still has reservations about whether the IDI and projects like it will actually change the training status quo. “These datasets will only have a positive impact if they’re used, probably in conjunction with licensing other data, to replace scraped copyrighted work. If they’re just added to the mix, one part of a dataset that also includes the unlicensed life’s work of the world’s creators, they’ll overwhelmingly benefit AI companies,” he says.
