John B. Quinn is the founder of Quinn Emanuel Urquhart & Sullivan LLP, the world’s largest law firm devoted solely to business litigation.
Would Michelangelo still have been as great had he been born with a different set of tools at his disposal? How do you separate the art from the artist? What are the boundaries of the artisan, and how much do those tools play a role in the final output?
These questions are not novel. But at no point in history have they been more relevant with the recent surge of generative AI products, from the image-generating DALL-E 2 to text-creating ChatGPT, which have opened up a flurry of fresh legal challenges. The following offers a brief overview of the key intellectual property implications it raises for businesses assessing what is original and what can be construed as “inspired.”
What Is Generative AI?
Generative AI is a subset of artificial intelligence and machine learning where a computer program learns complex algorithms to create new works such as images, text and music. Two common techniques used are generative adversarial networks (GANs) and autoencoders. GANs use two AI programs; the first generates new work based on a training set and the second checks whether the generated work is newly created or part of the set until the first can be used reliably as a standalone generative AI.
Autoencoders use two AI programs: An encoder, which reduces work to a small representation called “latent vectors,” and a decoder that then attempts to expand the latent vectors back into the original work; the difference between the two is used to train the system to generate new material and modify input material.
The Issue Of Copyright
Generative AI developers often use copyrighted material to train their tools without obtaining a license. However, ongoing litigation is now investigating whether such use constitutes an infringement of the copyright owner’s rights and whether they have the legal right to use existing works to train their algorithms, raising concerns about fair and transformative use.
The Legalese Of Fair Use
There are certain exceptions within copyright law that allow for creativity and innovation. In the U.S., the fair use doctrine permits the use of copyrighted work for purposes such as criticism, teaching, research and news reporting.
One of the key components of fair use is transformative use, which tests whether you can use a copyrighted work in a new and innovative way that was not originally intended, like training an AI to create art.
When determining whether a particular use of a copyrighted work is “fair,” one of the central pillars on which the whole argument rests is transformative use, gauging whether the original work has been altered sufficiently to become new and different and no longer qualifies as an infringement.
Not being a precise calculation, fair use can be claimed as a defense only when accused of infringement. For example, when Google was sued for scanning books and displaying snippets of texts in search results, their victory in court set a precedent for future cases. I predict many generative AI companies using this precedent to argue that the courts have ruled that ingesting content or code without permission to create new tools is considered a transformative use.
AI developers may also argue that using someone else’s work as an internal database for training their generative AI tools constitutes a transformative purpose, recognized by the Supreme Court ruling where copying parts of the Java API to create a new platform for programmers was deemed transformative.
Recently, the U.S. Copyright Office struggled with this issue, initially accepting Kristina Kashtanova’s “AI-assisted graphic novel,” Zarya of the Dawn, as registrable but later reversing course. Ms. Kashtanova contested the attempted revocation, analogizing generative AI tools to the use of a camera to create photographic images (accepted as copyrightable since the 19th century).
Ultimately, she was granted partial copyright for her text and artwork selection, but the AI-generated images were not protected as they were found “not the product of human authorship.”
Implications For Business Leaders
The emergence of generative AI systems raises various concerns, many of which have not been extensively explored. As a result of the lack of established legal and jurisprudential guidance, it’s best to approach these developments cautiously and utilize past experiences.
For businesses looking to explore the potential of incorporating generative AI into their core operations, one way is to start by experimenting with it at the hackathon level. This can allow you to gain some familiarity with the technology and its potential benefits. As generative AI becomes more accessible, you can integrate it into your core operations.
Another option is to purchase generative AI capabilities from startups that specialize in purpose-built tools. This focus on AI for specific uses can be a cost-effective way for you to leverage the benefits of generative AI without investing in expensive in-house development. As the technology becomes more democratized, these options are likely to become more widely available and accessible to businesses of all sizes.
Navigating The Uncertainty
It is too early to predict where exactly this is heading and how it will ultimately affect business decisions. Given the prevailing uncertainty surrounding generative AI systems, legal actions brought against such systems, including the lawsuit against Microsoft, OpenAI and their Copilot code-generating system, will have a significant impact on the approach taken by developers, and I see the court’s ruling substantially affecting how these models are trained.
There’s no denying that generative AI has opened the floodgates, and we’ve only just started to see how this new technology will fold into the legal world. Business leaders will have to watch the courts carefully to learn where the line between man and machine will be drawn.
If they prohibit the use of copyrighted material to train large-scale models, it could be a significant setback for developers. Conversely, if the courts allow for the use of any data—copyrighted or not—it will have significant implications for those who own the images and other properties used to train these systems. For now, I think it’s reasonable to require developers of generative AI systems to obtain permission before using any data.
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