Publishers file brief in ‘controlled digital lending’ case
March 28, 2024
On 15 March, publishers who successfully sued Internet Archive (IA) in relation to its ‘controlled digital lending’ program filed their brief opposing IA’s appeal.
There is information about the case, and the publishers’ position, in the statement from the Association of American Publishers here.
The District Court’s March 2023 decision, finding that IA had infringed copyright, is here. IA digitised 3.6 million books covered by copyright, which it ‘lent’ to IA patrons. The number of copies ‘lent’ at any given time was tied to the number of physical copies in IA’s collection and/or those of its partner libraries. IA said its program was allowed by the ‘fair use’ provision in US copyright law. The Court disagreed, saying:
At bottom, IA’s fair use defense rests on the notion that lawfully acquiring a copyrighted print book entitles the recipient to make an unauthorized copy and distribute it in place of the print book, so long as it does not simultaneously lend the print book. But no case or legal principle supports that notion. Every authority points the other direction.
A range of people and organisations have filed ‘amicus’ briefs, supporting the District Court’s finding:
- associations for writers and photographers (here)
- former government officials, former judges, intellectual property scholars (here and here)
- Copyright Alliance (here)
- associations representing publishers, news media publishers, film makers, music publishers and recording companies (here and here)
Australian copyright law does not include an equivalent to the ‘fair use’ exception in US copyright law. It has been considered from time to time (and is still proposed by some in the tech, library and education sectors), but has been rejected by both Coalition and Labor governments. It is currently being claimed in the US in court cases against multinational technology companies brought by writers, artists and others, in relation to digitisation of content without permission or payment to ‘train’ Large Language Models (LLMs) for Artificial Intelligence (AI).