(A.I.) Drake, The Weeknd, and the Future of Music

By: Melissa Torres

A new song titled “Heart on My Sleeve” went viral this month before being taken down by streaming services. The song racked up 600,000 Spotify streams, 275,000 YouTube views, and 15 million TikTok views in the two weeks it was available. 

Created by an anonymous TikTok user, @ghostwriter977, the song uses generative AI to mimic the voices of Drake and The Weeknd. The song also featured a signature tagline from music producer Metro Boomin. 

Generative AI is a technology that is gaining popularity because of its ability to generate realistic images, audio and text. However, concerns have been raised about its potential negative implications, particularly in the music industry, because of its impact on artists. 

Universal Music Group (UMG) caught wind of the song and had the original version removed from platforms due to copyright infringement. 

UMG, the label representing these artists, claims that the Metro Boomin producer tag at the beginning of the song is an unauthorized sample. YouTube spokesperson Jack Malon says, “We removed the video after receiving a valid copyright notification for a sample included in the video. Whether or not the video was generated using artificial intelligence does not impact our legal responsibility to provide a pathway for rights holders to remove content that allegedly infringes their copyrighted expression.”

While UMG was able to remove the song based on an unauthorized sample of the producer tagline, it still leaves the legal question surrounding the use of voices generated by AI unanswered. 

In “Heart on My Sleeve”, it is unclear exactly which elements of the song were created by the TikTok user. While the lyrics, instrumental beat, and melody may have been created by the individual, the vocals were created by AI. This creates a legal issue as the vocals sound like they’re from Drake and The Weeknd, but are not actually a direct copy of anything. 

These issues may be addressed by the courts for the first time, as initial lawsuits involving these technologies have been filed. In January, Andersen et. al. filed a class-action lawsuit raising copyright infringement claims. In the complaint, they assert that the defendants directly infringed the plaintiffs’ copyrights by using the plaintiffs’ works to train the models and by creating unauthorized derivative works and reproductions of the plaintiffs’ work in connection with the images generated using these tools.

While music labels argue that a license is required because the AI’s output is based on preexisting musical works, proponents for AI maintain that using such data falls under the fair use exception in copyright law. Under the four factors of fair use, advocates for AI claim the resulting works are transformative, meaning they do not create substantially similar works and have no impact on the market for the original musical work.

As of now, there are no regulations regarding what training data AI can and cannot use. Last March, the US Copyright Office released new guidance on how to register literary, musical, and artistic works made with AI. The new guidance states that copyright will be determined on a case-by-case basis based on how the AI tool operates and how it was used to create the final piece or work. 

In further attempts to protect artists, UMG urged all streaming services to block access from AI services that might be using the music on their platforms to train their algorithms. UMG claims that “the training of generative AI using our artists’ music…represents both a breach of our agreements and a violation of copyright law… as well as the availability of infringing content created with generative AI on DSPs…” 

Moreover, the Entertainment Industry Coalition announced the Human Artistry Campaign, in hopes to ensure AI technologies are developed and used in ways that support, rather than replace, human culture and artistry. Along with the campaign, the group outlined principles advocating AI best practices, emphasizing respect for artists, their work, and their personas; transparency; and adherence to existing law including copyright and intellectual property. 

Regardless, numerous AI-generated covers have gone viral on social media including Beyoncé’s “Cuff It” featuring Rihanna’s vocals and the Plain White T’s’ “Hey There Delilah” featuring Kanye West’s vocals. More recently, the musician Grimes recently shared her support toward AI-generated music, tweeting that she would split 50% royalties on any successful AI-generated song that uses her voice. “Feel free to use my voice without penalty,” she tweeted, “I think it’s cool to be fused [with] a machine and I like the idea of open sourcing all art and killing copyright.”

As UMG states, it “begs the question as to which side of history all stakeholders in the music ecosystem want to be on: the side of artists, fans and human creative expression, or on the side of deep fakes, fraud and denying artists their due compensation.”

While the music industry and lawyers scramble to address concerns presented by generative AI, it is clear that “this is just the beginning” as @ghostwriter977 ominously noted under the original TikTok posting of the song. 

No One Should Own Exclusively AI Generated Art

By: Jacob Alhadeff

On February 14, 2022, the Copyright Review Board (CRB) rejected Physicist Stephen Thaler’s claim for a copyright of his algorithm’s “authorship” because a “human being did not create the work.” On September 15, 2022, Kris Kashtanova received a copyright for their comic book Zarya of the Dawn, in which all of the art was AI generated, but Kris created the other aspects of the book. The difference in treatment is likely down to questions of originality, authorship, and simply that one work required human creativity while the other was effectively the work of a computer. Though these legal arguments are compelling in themselves, a necessary and implicit policy rationale seldom explicitly recognized by the law deserves highlighting — the relationship between work and incentives. Here, copyright incentivizes Kashtanova’s creative human work while reasonably denying that incentive to Thaler’s exclusively AI generated art. 

AI art, AKA generative art, uses machine learning (ML) algorithms that have been trained on billions of images frequently from licensed training sets and images publicly available on the internet. The images these algorithms use are frequently copyrighted or copyrightable. Users then type in a phrase, “carrot parrot,” for example, and a unique image is generated in seconds. Creating novel art can now be as simple as an image search on Google. This technology has been in the works for many years, but recently, platforms like DALL-E, Midjourney, and Stable Diffusion increased the volume of training data from millions to billions of parameters and the emergent result was an exponentially better output. In response, on October 17, 2022, Stable Diffusion announced the completion of a $101M seed round at a $1B valuation. Sequoia Capital then posted a blog suggesting that generative AI could create “trillions of dollars of economic value.” The future of Generative AI looms large, and at the very least promises to expose unexplored ambiguities in copyright. 

Functionally, in generative art there are two primary entities that may be incentivized through copyright — the programmer or the user. The programmer may have spent many hours writing and training the algorithm so that the algorithm may quickly create novel works of art. The user of the algorithm, on the other hand, is “the person who provides the necessary arrangements,” basically the person who prompts the program with a phrase. Providing either of these entities a copyright to exclusively generated art ineffectively balances incentives and ignores the purpose of copyright. 

Incentives and the Purpose of Copyright 

Copyright’s purpose is to “promote the progress of Science and useful Arts.” The Constitutional basis for copyright is therefore explicitly utilitarian. The Supreme Court has expanded on this language, suggesting that copyright’s purpose is to (1) “motivate the creative activity of authors and inventors by the provision of special reward” and (2) “to stimulate artistic creativity for the general public good.” Justice Ginsburg found that copyright’s dual purposes are mutually reinforcing because the public is served through copyright’s individual incentive. This mirrors James Madison’s claim regarding copyright, that “the public good fully coincides in both cases [copyright and patent] with the claims of individuals.” At its core, copyright is a monopoly-based incentive to create art to further public welfare. This incentive is at least implicitly predicated on the notion that creating valuable creative works is not easy, and therefore requires or deserves incentivizing. If improper law and policy are adopted, then Generative AI has the possibility to throw a wrench in this balancing of incentives.

The now rightfully defunct “sweat of the brow” copyright standard awarded a copyright partially because of the amount of work that went into the effort. One reason “sweat of the brow” was flawed was because it meant that facts themselves could be copyrighted if it took substantial work to attain those facts. The ability to copyright a fact “did not lend itself to support[ing] [] the public interest” and the standard was discarded. Though improper, the underlying concept was not entirely baseless. If the Constitutional purpose of copyright is to provide incentives to artists for public benefit, then copyright law must balance incentives, which implicitly balances work versus reward. 

Incentives are not absolute but are contextual and must at least tacitly recognize the difficulty of the act the incentive intends to induce. ‘Energy in’ must be somewhat commensurate with ‘value out’ — otherwise, the incentive structure is misaligned. This balancing of incentives is one of the reasons why a perpetual copyright is unconstitutional. If a copyright holder holds this monopoly right too long after its initial creation, they are rent-seeking, and the incentive that copyright provides far overshadows the public benefit. Rent-seeking is growing one’s wealth without “creating new wealth,” which has pernicious societal effects. For this reason, courts have determined that no amount of creativity, originality, or work merits an infinite monopoly on a creative work. 

Exclusively Generated Art Should Enter The Public Domain

Neither the user nor the programmer should receive a copyright for exclusively generated art, in part because doing so would misalign incentives. To be overly reductive, incentivizing someone to dedicate their life to an artistic craft requires a substantial incentive — a copyright for example. By contrast, if the effort required to create the art is effectively null (typing a prompt into generative AI), then the incentive required to promote the useful art is effectively null. As such, the law should not be reticent to reduce or eliminate the incentive for someone to type five words into a generative AI and provide a public benefit by creating exclusively generated art. Importantly, this reasoning excludes an artist’s creations that use generative AI as a tool or a component of their work – these artist’s works deserve copyright’s protection. Given that without any guarantee of copyright protection, over 1.5 million users are creating 2 million images a day using Dall-E, current evidence suggests that generative art users are not concerned about a monopoly on the economic returns for their creations. Lawmakers should not be concerned either. 

The owners of the generative AI algorithm should not receive a copyright for every work generated by their algorithm. Some in intellectual property suggest that AI generated art should be copyrightable because without protection, there will be a “chilling effect on investment in automated systems.” The argument is basically that if the owner of a generative art algorithm cannot hold a monopoly on the generated art, then there will be insufficient incentive to continue investing in automated systems. This ignores the concept of Software as a Service and the present reality that machine learning algorithms are currently effectively contributing to lucrative business models without guarantees of copyright protection. Relevantly, Stable Diffusion is valued at $1B.  

Further, a world where the algorithm’s owners automatically have a valid copyright claim could completely undermine the market for art. Similar to how no amount of work can justify a perpetual copyright, no amount of work could justify a handful of entities with machine learning algorithms copyrighting a substantial proportion of modern artistic creation. While generative art may simply become another tool for artistry, it is conceivable that someday the world’s human artists would not compare to the volume of work accomplished by ML algorithms. Lawmakers should not reduce artistic markets to whoever can create or purchase the most effective machine-learning algorithms.