AI Copyright Law Must Also Account for the Creators/Users/Prompters

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Instead of pitting artists and content creators against AI technology developers the debates about the future of creativity and copyright should be more nuanced, argues attorney Derek Slater, founding partner of Proteus Strategies.

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Many artists and content creators are users and beneficiaries of AI tools, and so the way these tools are regulated will impact them, too.

Consider the introduction of the camcorder, the mobile phone, and platforms like YouTube. All were demonized in some quarters as a threat to artistic creation by democratizing access to media, yet all have enriched our culture. So, generative AI is the same, Slater writes in an op-ed for Tech Policy Press.

“We see a familiar cycle – new technology democratizes creativity and enables a variety of new types of uses; initially, it’s seen at worst as a threat to art and artists, and, at best, marginal; and over time, it helps foster new forms of creativity and opportunities for creators to find audiences and make money.”

The core copyright concern with generative AI is that many tools are trained on massive datasets that contain copyrighted works, where this training has not been specifically licensed.

Slater contends that by keeping the interests of creator-users in mind, we can better arbitrate between what copyright should allow and prohibit.

“No creator develops their craft in a vacuum. Everyone learns by engaging with past works. You might walk around a museum and read painting manuals to learn how to create your own Surrealist art. Or you might watch classic horror movies in order to create your own take on the genre. Copyright has always permitted this sort of behavior, so long as the resulting creative output doesn’t copy directly from past expression or create something substantially similar to preexisting expressions.”

That doesn’t mean all generative AI tools should necessarily be permissible in every circumstance. Legal scholar Mehtab Khan and AI researcher Alex Hanna, in their more critical take on these tools, note a tougher call would be a system trained on a particular singer’s work in order to specifically generate songs like hers.

While style is not generally protected by copyright, the facts of each case will matter. For Slater, the key question is whether the tools are designed to substitute for particular creative expressions, rather than enabling new expressions and building on pre-existing ideas, genres, and concepts.

Someone can use a general purpose tool like Midjourney to create a work that is substantially similar to an existing copyright work. However, that shouldn’t mean the tool itself is infringing per se, as opposed to the user of the tool.

Slater says, “building on existing legal approaches, liability for the tool will depend on whether and how the tool developer or service provider knows about, contributes to, controls, and financially benefits directly from infringement.”

Addressing concerns that AI is reinforcing existing tech market structure he argues that extending copyright to further limit training on copyrighted works is unlikely to help and may even hurt creators of all stripes.

In a post examining AI art generation and its impact on markets, author Cory Doctorow and policy advocate Katherine Trendacosta imagine a world in which all AI training on copyrighted works must be licensed, and explain how this would be a “pyrrhic victory” for artists. That’s because, media markets are also highly concentrated (in part due to copyright itself), and the licensing fees would accrue to those corporations, not to artists.

Moreover, only those tech companies with substantial resources would be able to afford such licenses, reinforcing concentration in that sector.

“The solution to monopoly concerns in tech is not, then, to beef up the government-granted monopoly of copyright, but rather to apply other policy solutions, such as competition and privacy laws,” Slater says.

“The impact on labor markets is a real concern, but it’s also important to recognize that foreclosing generative AI also has an impact on creator-users of those tools.”

As one example, if you look at artist Kris Kashtanova’s tutorials, it’s apparent that generative AI can involve far more of a craft than simply clicking a button.

“People are right to call out the need to think about the impact of these tools on existing artists and content creators, and the political economy of the current tech sector,” says Slater. “But a full accounting can and should factor in the creator-users of these tools as well, both the ones that are emerging today and those that may come in the future.”