Tech

Internet Archive Loses Appeal in Copyright Case Against Major Publishers

Photo by Rafael Henrique/SOPA Images/Shutterstock.

A few years ago, as COVID-19 lockdowns raged on, the nonprofit Internet Archive created a temporary “National Emergency Library” of millions of books to help teachers and students access educational materials. The National Emergency Library suspended the usual waitlist between borrows, allowing anyone to borrow a book even if someone else had already borrowed it.

The site was sued by four major book publishers who claimed that the Internet Archive’s digital book lending practices infringed on publisher and author copyrights. 

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Typically, publishers and authors get a cut of the profits on the initial sale of a physical book, but they license e-books rather than sell them outright. A digital library is allowed to lend a digital copy of a physical book as long as it pulls the physical copy of the same book off of the shelf, ensuring that only one copy of the book was lent at a time. 

Unlike traditional digital library lending, the National Emergency Library allowed unlimited simultaneous borrowing of its titles, which were scanned copies rather than licensed e-books. Authors or publishers could request to have their books removed from the program, but their work was included in the first place without their explicit permission.

In 2023, the Internet Archive lost that historic copyright case and vowed to appeal

The ruling on that appeal just dropped. The Internet Archive lost again.

The Internet Archive offers several services outside of its book lending program. Some fear a trickle-down effect that could impact other parts of its operations, including the Wayback Machine, a digital snapshot of websites over the years. The Archive is also home to many thousands of hours of archival audio, video, and even video games that are no longer in circulation. 

You also have to wonder if the same enthusiasm for going after the Internet Archive will be applied to artificial intelligence companies using copyrighted materials to train their AI models.