AI creative: Who owns the works of great language models?

Prof. Dr. Anne Lauber-Rönsberg, Professor of Law at the Technical University of Dresden and member of Plattform Lernende Systeme

Large language models like ChatGPT can now write texts that are indistinguishable from human texts. ChatGPT is even cited as a co-author in some scientific articles. Other AI systems such as Dall-E 2, Midjourney, and Stable Diffusion generate images based on brief linguistic instructions. Artists, as well as image agency Getty Images, accuse the company behind popular image generator Stable Diffusion of using their works to train the AI without their consent, and have filed lawsuits against the company.

Back in 2017, researchers at Rutgers University in the US showed that in a comparison of AI-generated and human-created paintings, subjects not only failed to recognize the AI-generated products as such, but even judged them superior to the human-created paintings by a narrow majority.

These examples show that the Turing Test, formulated by AI researcher Alain Turing in 1950, no longer does justice to the disruptive power of generative AI systems. Turing posited that an AI can be said to have a reasoning capacity comparable to a human if, after chatting with a human and an AI, a human cannot properly judge which of the two is the machine. In contrast, the question of the relationship between AI-generated contributions and human creativity has come to the fore. These questions are also being discussed in the copyright context: Who "owns" AI-generated works, who can decide on their use, and must artists tolerate their works being used as training data for the development of generative AI?

Copyright: human vs. machine?

So far, AI has often been used as a tool in artistic contexts. As long as the essential design decisions are still made by the artist himself, copyright also arises in his favor in the works created in this way. On the other hand, the situation is different under continental European copyright law if products are essentially created by an AI and the human part remains very small or vague: Asking an AI image generator to produce an image of a cat windsurfing in front of the Eiffel Tower in the style of Andy Warhol is unlikely to be sufficient to establish copyright in the image. Products created by an AI without substantial human intervention are free of copyright and can thus be used by anyone, provided that no other ancillary copyrights exist. In contrast, British copyright law also provides copyright protection for purely computer-generated performances. These differences in design have triggered a debate about the meaning and purpose of copyright. Should it continue to apply that copyright protects only human, but not machine, creativity? Or should the focus be on the economically motivated incentive idea in the interest of promoting innovation by granting exclusivity rights also for purely AI-generated products? The fundamental differences between human creativity and machine creativity argue for the former view. Humans' capacity for experience and sensibility, an essential basis for their creative activity, justify their privileging by an anthropocentric copyright law. In the absence of creative abilities, AI authorship is out of the question. To the extent that there is a need for this, economic incentives for innovation can be created specifically through limited ancillary copyrights.

The question of the extent to which works available on the net may be used as training data to train AI must also be appropriately balanced between the interests of artists and the promotion of innovation. Under European copyright law, such use, known as text and data mining, is generally permitted if the authors have not excluded it.

Increasing demands on human originality

However, these developments are nevertheless likely to have indirect implications for human creators as well. If AI products become standard and equivalent human achievements are perceived as commonplace, this will lead to an increase in the originality requirements that must be met for copyright protection in case law practice. Also in factual terms, it is foreseeable that human performances such as translations, utility graphics or the composition of musical jingles, will be replaced more and more by AI.

Even beyond copyright law, machine co-authorship for scientific contributions should be rejected. Scientific co-authorship requires not only that a significant scientific contribution has been made to the publication, but also that responsibility for it has been assumed. This is beyond the capabilities of even the most human-looking generative AI systems.