Understanding the Boundaries of Fair Use in AI

AI companies’ reliance on “fair use” to train their models is under fire, with lawsuits pushing for stricter copyright protection. If fair use fails, some models may even face complete shutdown, marking a major turning point for the industry.

Understanding the Boundaries of Fair Use in AI

by Mick Kiely, CEO of IAIAI Technologies

For years, AI companies have leaned on the doctrine of “fair use” to justify their extensive scraping of copyrighted material for training their models. They’ve argued that their use of music, books, articles, and images is transformative and doesn’t harm the market for the original works. But in recent months, this argument has begun to show more than a few cracks.

Over the past year, there has been a surge in AI-related lawsuits, with content creators and publishers taking a stand against what they see as blatant copyright infringement. Cases like the New York Times vs. OpenAI lawsuit have rang out across the industry, challenging the very foundation of how AI models are trained.

As courts consider the intricacies of AI training and its impact on intellectual property rights, the “fair use” argument may well crumble. The sheer volume of copyrighted material used in training AI models goes far beyond what has in the past been considered “fair use.” Also, many AI models are used for commercial purposes, weakening the “fair use” claim, as profit is usually not allowed under fair use. More important, however, is the fact that as AI-generated content becomes more prevalent, it directly competes with and devalues the very works on which it was trained.

The Fall Of Fair Use

If the “fair use” argument does collapse, many AI companies will find themselves in even deeper waters. Their prized models, trained on vast amounts of stolen copyright, will themselves become legal liabilities. Companies once hailed as pioneers of AI innovation may suddenly look like nothing more than old leaky pirate ships, drifting toward the edge of a world they once insisted was flat, their AI models  irreversibly contaminated by copyright infringement.

The fall of “fair use” will likely force a massive restructuring of AI development. Companies will have to develop new, ethically sourced datasets, invest heavily in legal defenses and settlements, and even explore alternative training methods that don’t rely on copyrighted material. This shift will be costly, time-consuming, and could push some AI companies like OpenAI into an ice age of their own making.

“these models, like sponges, absorb every piece of information they encounter”

But that’s only the tip of the proverbial iceberg and here’s why, these models, like sponges, absorb every piece of information they encounter during training. The neural networks that form the bones of these AI systems integrate this knowledge so deeply that it becomes an inseparable part of their functionality. There’s no “undo” button—no way to selectively remove specific influences without destroying the entire model.

Imagine a student who has memorized a book word for word. Now, try to make that student “unlearn” specific passages. It’s impossible. This is precisely the predicament we face with AI models trained on unlicensed copyrighted material.

All of this raises a huge problem for both AI companies and rights holders: the inability to “opt out”. Once a work has been ingested by an AI model, its influence cannot be removed, regardless of the copyright holder’s wishes.

We would see the emergence of hybrid generative AI models trained on both legally licensed and unauthorized copyrighted material, thus presenting a whole new problem. These hybrid models would straddle the line between legality and infringement and pose greater challenges for developers, users, and copyright holders. The technical challenges of data separation and output attribution, between legitimately sourced and illicitly obtained training data would be enormous.

Given the impossibility of selective unlearning and the violation of copyright holders’ rights, we may be left with only one option: the complete destruction of AI models that have touched unlicensed copyrighted material.

This may seem extreme, but it may be the only way to put things right and maintain the integrity of copyright law. The alternative could be novel innovation, where training sources can be detected, attributed, and compensated at the time an AI work is generated.

One thing is clear, the future of AI and intellectual property still hangs in the balance. In the interim, we must act to ensure that the rights of creators are upheld while AI searches for firmer ground. 

The destruction of current AI models may seem like a setback, but it could be a necessary step toward a more ethical and legally sound AI future—a future that truly benefits humanity.