I think it's really disingenuous to mention the DeviantArt/Midjourney/Runway AI/Stability AI lawsuit without talking about how most of the infringement claims were dismissed by the judge.
You should read this letter by Katherine Klosek, the director of information policy and federal relations at the Association of Research Libraries.
Why are scholars and librarians so invested in protecting the precedent that training AI LLMs on copyright-protected works is a transformative fair use? Rachael G. Samberg, Timothy Vollmer, and Samantha Teremi (of UC Berkeley Library) recently wrote that maintaining the continued treatment of training AI models as fair use is “essential to protecting research,” including non-generative, nonprofit educational research methodologies like text and data mining (TDM). If fair use rights were overridden and licenses restricted researchers to training AI on public domain works, scholars would be limited in the scope of inquiries that can be made using AI tools. Works in the public domain are not representative of the full scope of culture, and training AI on public domain works would omit studies of contemporary history, culture, and society from the scholarly record, as Authors Alliance and LCA described in a recent petition to the US Copyright Office. Hampering researchers’ ability to interrogate modern in-copyright materials through a licensing regime would mean that research is less relevant and useful to the concerns of the day.
It should be fully legal because it's still a person doing it. Like Cory Doctrow said in this article:
Break down the steps of training a model and it quickly becomes apparent why it's technically wrong to call this a copyright infringement. First, the act of making transient copies of works – even billions of works – is unequivocally fair use. Unless you think search engines and the Internet Archive shouldn't exist, then you should support scraping at scale: https://pluralistic.net/2023/09/17/how-to-think-about-scraping/
Making quantitative observations about works is a longstanding, respected and important tool for criticism, analysis, archiving and new acts of creation. Measuring the steady contraction of the vocabulary in successive Agatha Christie novels turns out to offer a fascinating window into her dementia: https://www.theguardian.com/books/2009/apr/03/agatha-christie-alzheimers-research
The final step in training a model is publishing the conclusions of the quantitative analysis of the temporarily copied documents as software code. Code itself is a form of expressive speech – and that expressivity is key to the fight for privacy, because the fact that code is speech limits how governments can censor software: https://www.eff.org/deeplinks/2015/04/remembering-case-established-code-speech/
That's all these models are, someone's analysis of the training data in relation to each other, not the data itself. I feel like this is where most people get tripped up. Understanding how these things work makes it all obvious.
You should read these two articles from Cory Doctorow. I'd like to hear your thoughts.
https://pluralistic.net/2024/05/13/spooky-action-at-a-close-up/#invisible-hand
https://pluralistic.net/2023/02/09/ai-monkeys-paw/