Who should own what AI produces?
The role of AI in media production leaves the question of ownership.
An entry by Kimberly Schnur and Paul Heß
Friday, November 03, 2023
Media, Technology and Society
Everyone is talking about artificial intelligence (AI). LinkedIn is filled with amateur experts on the topic, YouTube is promoting get-rich schemes using ChatGPT, and discussions on impending doom scenarios are filling classic media channels. While this is happening, a real, tangible concern regarding AI is rising in different fields: The question of ownership over media products produced by AI. The writers- and actors unions in the United States, for example, were, and still are as of the time of writing this post, on strike because of this topic. Questions over who owns a text generated by AI that was based on the work of a writer or who owns the computer-generated likeness of an actor are causing heated discussions.
More examples in different fields of work are found weekly. This begs the question: “Who should own what AI produces?”. Because of the country’s strong cultural influence on this topic, the United States plays a major role.
How does AI work, how should it work, and how can humans deal with its consequences?
Artificial intelligence is a broad term describing different technologies, like machine learning or natural language processing. All of them have in common that they need data to produce an outcome. This data can come from developers/AI companies themselves, or it can be obtained from the internet or other sources. That’s where the basis for existing ownership issues can be found. From an ethical point of view, everyone participating in the creation of a product should be properly credited. Furthermore, there’s a widespread belief that a human is needed to create art. The US copyright law itself does not explicitly state who can be granted copyright, and thus ownership. It is therefore unclear whether AI could also be defined as an author and be protected by copyright laws, for example, when it creates content without the help of a human. But delving further than this initial definition, the U.S. Copyright Office has gone on to state that authorship will only be granted if a human created the content.
Karl Marx has already discussed the question of ownership over the means of production. Based on his theory, technologies like AI could enhance living conditions for workers and people overall, but they are often used to increase the value for the capitalist who owns them. Following this, ownership in a capitalist system lies with the capitalist. However, as an alternative, claims could be made by the workers training the AI (directly or indirectly).
Expert Opinions
Experts on the topic, like people working in AI industries, academics, creators, or lawmakers, have voiced their opinion in numerous newspaper articles. A look at these expert opinions within a content analysis indicates, that AI can be seen as a new technology that creates opportunities for artists, but is limited by the way it works. It relies on data created by humans to predict outputs, which can be problematic as this is frequently taken from the internet without regard for ownership of the original creators.
Frequently, no consent is given by artists to train AI on their creations, and AI companies even breach copyright laws, which is why some experts argue that ownership should not lie with AI companies. Furthermore, the argument has been
raised that the value of art and work is tied to humans creating intellectual property. When AI creates something, the individual using AI still has responsibility. However, remixing ideas has always been a part of human history, and this idea could thus also apply to AI practices. This is especially important in a commercial context.
AI should enhance the imaginative power of humans, yet it is currently being used to replace artists by taking over some of their work. Especially, experts working in the field of law see transparency as essential for finding solutions. Regulators need to understand how AI works, and companies should make this more transparent. Then, industry standards, guidelines, or even government intervention can be possible.
Thus, while not many clear legal positions were expressed, one prominent aspect was that protected intellectual property can only be created by humans as of now.
How people who create music, visual art or text content are facing these issues
Relevant cases that were able to be defined are AI creating visual art forms, AI writing music, AI creating text content, and AI regulation. Here, many similarities can be found, but also some differences. While fears of AI replacing humans are expressed in all cases, the positive evaluation differs. The emergence of AI is more often compared to past evolutions of technologies within the area of music than in the other areas. Beyond these differences, the discussion around the topic of AI and ownership focuses on similar aspects across all cases. Ethics took center stage within this discussion, and the fear of humans being replaced by AI was raised several times, especially in the cases regarding creating art. Experts seem unanimous on how AI is changing the workplace. Connected to that, concerns about the origin of the data being used to train these AI systems are also part of the discussion. A common position is that the output generated by AI based on this data still belongs to the original creators in some capacity. Regarding the current regulatory situation, experts across the different cases agreed that there is a lack of guidelines, even including the CEO of such an AI company, while calling for regulations to be established. This was especially in the case regarding regulation.
And now? What should be done?
Currently, the issue lies with people creating content that AI uses not being compensated correctly. This is where change needs to happen in the field of AI to
establish an ethical way of working with this technology and preventing monopoly ownership by one party. That is why, based on the analysis of expert opinions, the following next steps can be proposed:
- Voluntary submissions for AI training data, instead of stealing art and breaching copyright
- Compensation and partial ownership for artists that create content AI is trained on by AI companies
What does the future hold?
The development and establishment of regulations for how AI ownership is handled will be accelerated as the discussion no longer merely focuses on ethical concerns, but will have direct economic consequences (as seen in the writers and actors strike). When regulations are inevitably established, AI systems will still hold a lot of power, but might be viewed more positively by creators as a tool to expand their work. Who this work will ultimately be accredited to according to new regulations and standards is still up in the air. Looking at transparency, AI and its effects could be further studied by independent researchers to make how it works more transparent for lawmakers. Furthermore, a fair model of compensation for the various stakeholders could be determined.
References
Acemoglu, D., & Johnson, S. (2023, June 9). Big Tech Is Bad. Big A.I. Will Be Worse. The New York Times. https://www.nytimes.com/2023/06/09/opinion/ai-big-tech-microsoft-google-duo poly.html
Barnes, B., Koblin, J. & Sperling, N. (2023, July 13). Actors Join Writers on
Strike, Bringing Hollywood to a Standstill. The New York Times. https://www.nytimes.com/2023/07/13/business/media/sag-aftra-writers-strike.h tml (last accessed on August 2, 2023)
Chan-Olmsted, S. M. (2019). A Review of Artificial Intelligence Adoptions in the Media Industry. International Journal on Media Management, 21(3–4), 193–215. https://doi.org/10.1080/14241277.2019.1695619
Coscarelli, J. (2023, April 19). An A.I. Hit of Fake ‘Drake’ and ‘The Weeknd’
Rattles the Music World. The New York Times. https://www.nytimes.com/2023/04/19/arts/music/ai-drake-the-weeknd-fake.html
Draxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D. & Welsch, R. ( 2023). The AI Ghostwriter Effect: Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors. In ACM, New York, USA. 1 – 34.
Elteren, M. V. (2003). U.S. Cultural Imperialism: Today Only a Chimera. SAIS Review, 23(2), 169–188. https://doi.org/10.1353/sais.2003.0038
Federal Trade Commission. (2023). Federal Trade Commission Act. FTC.
https://www.ftc.gov/legal-library/browse/statutes/federal-trade-commission-act
(last accessed on July 27, 2023)
Floridi, L. (2020). The Fight for Digital Sovereignty: What It Is, and Why It Matters, Especially for the EU. Philosophy & Technology, 33(3), 369–378. https://doi.org/10.1007/s13347-020-00423-6
Fouseki, K. & Vacharopoulou, K. (2013). Digital museum collections and social media: ethical considerations of ownership and use. Journal of Conservation and Museum Studies, 11(1) , Article 5. 10.5334/jcms.1021209. https://doi.org/10.3390/math10152552
Fuchs, C. (2002). Soziale Selbstorganisation im informationsgesellschaftlichen Kapitalismus / 2 Krise und Kritik in der Informationsgesellschaft: Arbeiten über Herbert Marcuse, kapitalistische Entwicklung und Selbstorganisation. Books on Demand.
Next Move Strategy Consulting. (2023, January). Artificial intelligence (AI)
market size worldwide in 2021 with a forecast until 2030. NextMSC. https://www.nextmsc.com/report/artificial-intelligence-market (last accessed on July 27, 2023)
Rodgers, D. (2023, July 14). Shein’s secretive AI algorithm will steal your artwork. Dazed.
https://www.dazeddigital.com/fashion/article/60355/1/shein-mafia-barbie-ai-alg orithm-will-steal-your-artwork-lawsuit-fashion-news (last accessed on August 2, 2023)
Mukhamediev, R. I., Popova, Y., Kuchin, Y., Zaitseva, E., Kalimoldayev, A., Symagulov, A., Levashenko, V., Abdoldina, F., Gopejenko, V., Yakunin, K., Muhamedijeva, E., & Yelis, M. (2022). Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges. Mathematics, 10(15), 2552. https://doi.org/10.3390/math10152552
Nachtwey, O. & Staab, P. (2015, December). Die Avantgarde des digitalen Kapitalismus. eurozine, 21. https://www.researchgate.net/publication/292994969
OpenAI. (2022a, November 14). Sharing & publication policy
https://openai.com/policies/sharing-publication-policy#content-co-authored-wit h-the-openai-api-policy (last accessed on July 27, 2023) https://openai.com/blog/chatgpt
OpenAI. (2022b, November 30). Introducing ChatGPT.
https://openai.com/blog/chatgpt
Schlag, G. (2023). European Union’s Regulating of Social Media: A Discourse Analysis of the Digital Services Act. Politics and Governance, 11(3). https://doi.org/10.17645/pag.v11i3.6735
Taulli, T. (2019). Artificial Intelligence Basics: A Non-Technical Introduction.
Apress. https://doi.org/10.1007/978-1-4842-5028-0
Turvill, W. (2022, June 24). Top 25 US newspaper circulations: Print sales fall another 12% in 2022. Press Gazette. https://pressgazette.co.uk/news/us-newspaper-circulations-2022/ (last accessed on July 07, 2023)
United States Copyright Office. (2021). Compendium of U.S. Copyright Office practices § 101. 3d ed. 2021. Copyright. https://copyright.gov/comp3/chap300/ch300-copyrightable-authorship.pdf(last accessed on July 26, 2023).
United States Department of State. (2023). Intellectual Property Enforcement. State. https://www.state.gov/intellectual-property-enforcement/ (last accessed on July 27, 2023)
United States Patent And Trademark Office. (2023). Trademark, patent, or copyright. USPTO.
https://www.uspto.gov/trademarks/basics/trademark-patent-copyright (last accessed on July 27, 2023)
Fuchs, C. (2020). Communication and Capitalism: A Critical Theory. University of Westminster Press. https://doi.org/10.16997/book45
Veblen, T. (1898). The beginnings of ownership. American Journal of Sociology, 4(3), 352–356. https://doi.org/10.1086/210805