Legal uncertainty regarding AI and copyrights undermines both the AI industry and legal system generally

At the crux of this issue is the question of data use. What data do companies like OpenAI have a right to use, and what do concepts like “transform” really mean in these contexts? Questions like this, surrounding the data we train AI systems, or models, like ChatGPT on, remain a fierce academic battleground. The law often lags behind the behaviour of industry. If you’ve used AI to answer emails or summarise work for you, you might see ChatGPT as an end justifying the means. However, it perhaps should worry us if the only way to achieve that is by exempting specific corporate entities from laws that apply to everyone else. Not only could that change the nature of debate around copyright lawsuits like this one, but it has the potential to change the way societies structure their legal system. Fundamental questions Cases like this can throw up thorny questions about the future of legal systems, but they can also question the future of AI models themselves. The New York Times believes that ChatGPT threatens the long-term existence of the newspaper. On this point, OpenAI says in its statement that it is collaborating with news organisations to provide novel opportunities in journalism. It says the company’s goals are to “support a healthy news ecosystem” and to “be a good partner”. Even if we believe that AI systems are a necessary part of the future for our society, it seems like a bad idea to destroy the sources of data that they were originally trained on. This is a concern shared by creative endeavours like the New York Times, authors like George R.R. Martin, and also the online encyclopedia Wikipedia. Advocates of large-scale data collection – like that used to power Large Language Models (LLMs), the technology underlying AI chatbots such as ChatGPT – argue that AI systems “transform” the data they train on by “learning” from their datasets and then creating something new. Effectively, what they mean is that researchers provide data written by people and ask these systems to guess the next words in the sentence, as they would when dealing with a real question from a user. By hiding and then revealing these answers, researchers can provide a binary “yes” or “no” answer that helps push AI systems towards accurate predictions. It’s for this reason that LLMs need vast reams of written texts. If we were to copy the articles from the New York Times’ website and charge people for access, most people would agree this would be “systematic theft on a mass scale” (as the newspaper’s lawsuit puts it). But improving the accuracy of an AI by using data to guide it, as shown above, is more complicated than this. Firms like OpenAI do not store their training data and so argue that the articles from the New York Times fed into the dataset are not actually being reused. A counter-argument to this defence of AI, though, is that there is evidence that systems such as ChatGPT can “leak” verbatim excerpts from their training data. OpenAI says this is a “rare bug”. However, it suggests that these systems do store and memorise some of the data they are trained on – unintentionally – and can regurgitate it verbatim when prompted in specific ways. This would bypass any paywalls a for-profit publication may put in place to protect its intellectual property. Language use But what is likely to have a longer term impact on the way we approach legislation in cases such as these is our use of language. Most AI researchers will tell you that the word “learning” is a very weighty and inaccurate word to use to describe what AI is actually doing. The question must be asked whether the law in its current form is sufficient to protect and support people as society experiences a massive shift into the AI age. Whether something builds on an existing copyrighted piece of work in a manner different from the original is referred to as “transformative use” and is a defence used by OpenAI. However, these laws were designed to encourage people to remix, recombine and experiment with work already released into the outside world. The same laws were not really designed to protect multi-billion-dollar technology products that work at a speed and scale many orders of magnitude greater than any human writer could aspire to.