As AI-generated music becomes more accessible and ubiquitous within the music market, a new question looms: who actually owns the music that this technology produces? While the music-making process is solely machine learning, the data that these models train on often includes real, copyrighted music.
Such a complex web of data, ownership, and AI creativity raises a question that’s more relevant than ever: does AI music infringe on the intellectual property of its dataset? In this article, we explore the ethics and ownership of AI-generated music, examining how AI models learn from copyrighted data and the implications for artists and copyright holders.
Data as the Backbone of AI-Generated Music
AI models that create music rely on data—a lot of it. To recognize musical structure, understand melody, or replicate rhythm, models need extensive libraries of existing music to learn from. In AI development, this training process helps the model make sense of patterns, anticipate variations, and eventually generate music that sounds surprisingly close to human creations.
Unfortunately, the training data for many AI models includes copyrighted music. But why is this a problem? Well, if an AI model relies on existing music, it may inadvertently reproduce a number of unique elements from it, risking unauthorized replication and impacting the revenue and rights of the original artists. For example, recently, a song generated by Suno using the prompts "contemporary R&B" and "male singer" produced a song with globally-renowned artist Jason Derulo's exact iconic intro adlib.
This consideration is pivotal as the music industry balances innovation with the protection of creators' intellectual property. When an AI learns from an existing song, does it simply understand its structure, or is it replicating its essence? And does that learning process constitute overt copyright infringement?
The RIAA vs. Suno and Udio: A Case Study in AI Training Copyright Dilemmas
Perhaps most emblematic of the debate around AI training & IP was in June of this year, when the RIAA (Recording Industry Association of America) sued generative AI music platforms Suno and Udio. The essence of the RIAA's claim was that these companies infringed on copyright by using commercial music as training data, claiming this use profits from others’ creativity without any permission or recorded consent.
Both Suno & Udio are yet to make their training datasets public, although Udio CEO David Ding said that Udio trained on the best quality music that is "publicly available". Similarly, Suno recently admitted that its training data includes "essentially all music files of a reasonable quality that are accessible on the open internet". Nevertheless, the contention from both generative AI companies is that their usage falls under fair use, a legal concept that permits limited, transformative use of copyrighted work without licensing.
Ultimately, the results of this lawsuit will be crucial for setting legal standards on using copyrighted data in AI training, including both legislation which mandates transparency and licensing frameworks which allow original composers to be credited and compensated fairly.
Transparency, Detection and Licensing Solutions
The contention made by many within the industry is that, if music by commercial artists is part of an AI’s training set, this is something which should be disclosed to users, and the original artists should be credited. Industry players are even calling for training transparency reports that specify which data sets an AI model was trained on, helping listeners and copyright holders know if their work played a role in generating new content.
But this information doesn't tangibly benefit original creators without strict licensing frameworks in place. By clarifying the contributors to AI-driven songs, set-in-stone licensing frameworks could be established to provide more clarity. In essence, this would allow artists and copyright holders to get paid for the data (their music) that drives AI creation, while AI companies could develop their models with a reduced risk of litigation.
This is very much an ongoing conversation within the industry, and one which prompts a number of large questions. Should companies pay creators every time they feed a song into a model, or is a one-time license enough? And who should manage these licenses—the creators, labels, CMOs or a new entity? What type of license would this concern, and therefore, which royalties would go out to the original rights holders? Answering questions such as these would require major collaboration within the industry, as well as new legal standards for how music data is valued in AI training contexts.
The ability to actually detect AI music is a key first step, and without this ability, it's impractical to implement any of the aforementioned licensing frameworks. This is where MatchTune comes in. Our AI-generated music detection technology is able to recognize if a song has been generated by AI and, if it has, which platform it's been generated from. Drop a dataset of music, and in seconds you can understand more about the origins of each track. This technology marks the first step to establishing control over AI-generated music, and we're excited that it's now available to artists, publishers and rights holders alike.
You can learn more about this technology here.
Where Do We Go from Here?
The question of who owns AI-created art will likely shape the future of music itself. While the plethora of musical data available online enables AI to innovate, it undeniably challenges the rights of those who originated it. As the repercussions of cases like the RIAA vs. Suno and Udio unravel, the music industry will need to adapt and establish frameworks that respect both artistic integrity and technological progress.
Looking to detect infringing generative AI music online? Get in touch with MatchTune today.
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