{"id":436,"date":"2025-10-05T19:23:54","date_gmt":"2025-10-05T19:23:54","guid":{"rendered":"https:\/\/claremontlawjournal.sites.pomona.edu\/?p=436"},"modified":"2025-10-05T19:27:31","modified_gmt":"2025-10-05T19:27:31","slug":"regulating-ai-a-path-paved-by-musicians","status":"publish","type":"post","link":"https:\/\/claremontlawjournal.sites.pomona.edu\/?p=436","title":{"rendered":"Regulating AI: A Path Paved by Musicians"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Throughout his acclaimed and controversial career, the American musician known as Prince challenged the traditional power structures of the music industry by speaking out against the constraints of conventional label-to-artist contractual relations and vying for more creative and financial control over his work.[1]<\/sup><\/a> His public and lengthy legal battle with Warner Music, his label, exemplified the enduring tensions between artists seeking creative autonomy and labels\u2019 desire to maintain financial control. Leading up to the 1995 release of his studio album The Gold Experience, Prince detached himself from his Warner Music-owned stage name and changed his name to an unpronounceable symbol. His fans began to refer to him colloquially as either \u201cThe Artist Formerly Known as Prince,\u201d \u201cSymbol,\u201d or \u201cSquiggle.\u201d[2]<\/sup><\/a> This was a small but symbolic step in the decades-long legal battle between Prince and his label, a battle that illuminates the conflict between creative expression and corporate interests existing amid a landscape of discrimination and other cultural tensions.<\/p>\n\n\n\n

The socio-politically transformative capacity of music provides an interesting framework for examining how intellectual property (IP) law can adapt to the challenges presented by artificial intelligence (AI). Debates over artistic rights are a key source of tension between musical artists and their labels, and these debates are similar to ones about authorship and ownership in a new age of AI-written content both in the music industry and beyond it. With AI models now capable of generating lyrics, arrangements, setlists, and vocal tracks that convincingly mimic those generated by humans, IP legal experts and music industry professionals must work together to consider how best to respond. <\/p>\n\n\n\n

IP law today fails to adequately protect artists in an ever-evolving digital world. AI poses an additional challenge to this preexisting one, as it threatens to render existing protections obsolete. Old laws and media licensing regulations have struggled to keep up with these technological advancements, and as a result, creative industries must push for the development of IP law.       This paper uses music\u2019s technological evolution, particularly with regard to sampling culture, as a case study of how laws can adapt to the threat posed by a new wave of AI-generated content. Analyzing past cases concerning music licensing, reproduction, and ownership\u2013\u2013as well as more recent case involving AI and media production\u2013\u2013I argue the introduction of AI further complicates the already-muddied IP legal landscape, and I offer existing media regulations as potential frameworks for the future regulation of AI in creative industries. I argue that AI regulations concerning media production must balance the desire for technological innovation with the need for robust protections for humans; establish clear guidelines for the authorship and ownership of AI-generated content; and create flexible frameworks that can adapt to the rapidly evolving technological landscape. By first examining the relationship between IP law and technological disruption, legal experts can begin to develop a nuanced understanding of how IP law can better adapt to AI\u2019s evolution.<\/p>\n\n\n\n

I. Historical Background: Pirating and IP Law<\/strong><\/p>\n\n\n\n

IP law is intended to balance the protection of creators\u2019 rights with the public\u2019s ability to access creative works ranging from art to computer programs.[3]<\/sup><\/a> An example of this balance is copyright, which \u201cserves the public interest by helping to ensure that creators can earn a fair reward for their work, thus encouraging further creative endeavors.\u201d[4]<\/sup><\/a> Beginning in the early 1990s, the internet and the introduction of on-demand downloads, file sharing, and other digital subversions of traditional legal boundaries fundamentally disrupted IP law\u2019s relatively unchallenged function: protecting intangible assets.[5]<\/sup><\/a> Initially robust in regulating physical and analog media such as books, films, and audio recordings, this system soon struggled to handle the increasing number of possibilities for content creation and circulation.<\/p>\n\n\n\n

The evolution of digital piracy follows a clear trajectory: floppy disks containing unauthorized content gave way to peer-to-peer (P2P) file sharing on platforms such as Napster in the late 1990s, which then snowballed into more \u201csophisticated websites and illicit streaming devices\u201d being widely available by the 2010s.[6]<\/sup><\/a> This progression continues today with the widespread use of virtual private networks (VPNs) and torrenting sites such as Soap2Day and MyFlixer that circumvent traditional copyright enforcement mechanisms. These digital content sites evade the historically easy-to-follow paper trail and make the process of duplicating creative materials significantly easier. In the span of a few decades, in-groups of artisans with access to vinyl pressing equipment saw themselves outnumbered by the millions of people who now have access to compact disc (CD)-burning tutorials and digital video recorders. As media reproduction technologies become increasingly accessible, consumers have gained unprecedented control over how media\u2013\u2013and music in particular\u2013\u2013is copied, shared, and repurposed. Piracy floodgates eroded as sophisticated devices became more widely available, and consumers began to pirate and collage pre-existing songs.[7]<\/sup><\/a> The legal system\u2019s initial response to these challenges revealed the limitations of existing IP legal frameworks. Several landmark cases established essential precedents that continue to influence today\u2019s legal debates over AI-generated content in creative industries. <\/p>\n\n\n\n

For example, A&M Records, Inc. v. Napster, Inc.<\/em> directly addressed P2P file sharing networks, with the U.S. Court of Appeals for the Ninth Circuit ruling that Napster, a leading P2P application, had committed contributory and vicarious copyright infringement by facilitating unauthorized music sharing.[8]<\/sup><\/a> The court rejected Napster\u2019s defense of fair use and found that digital content distributors could be held liable for the activities of their users.[9]<\/sup><\/a> This judgement critically established that technological platforms\u2013\u2013and not only individual users\u2013\u2013bore responsibility for compliance with copyright law.[10]<\/sup><\/a> Additionally, MGM Studios, Inc. v. Grokster, Ltd<\/em>. extended this principle when the U.S. Supreme Court unanimously ruled in favor of the creation of the \u201cinducement rule,\u201d finding that P2P platforms could be held liable for encouraging and allowing copyright infringement to take place, even if they did not have specific knowledge of their users\u2019 violations.[11]<\/sup><\/a> This case marked a significant expansion of secondary liability in copyright law, demonstrating the legal system\u2019s attempt to adapt traditional principles to emerging technologies such as P2P file sharing. UMG Recordings, Inc. v. MP3.com, Inc.<\/em> further restricted digital content reproduction rights when the U.S. District Court for the Southern District of New York ruled that MP3.com\u2019s platform, which allowed users to access the MP3 files of CDs they already owned, constituted copyright infringement despite requiring proof of users\u2019 CD ownership.[12]<\/a> The court also rejected the argument that this service merely enabled \u201cspace shifting\u201d for legitimately purchased content, highlighting the tension between consumer convenience and copyright protection.[13]<\/sup><\/a> These cases reveal a legal system struggling to balance support for technological innovation with the need to protect creators\u2019 rights. While courts generally favored copyright holders, they also faced mounting challenges when attempting to enforce traditional copyright restrictions in an environment in which content reproduction and distribution could occur instantly, globally, and at little to no cost. The resulting legal uncertainty created a gap between formal copyright law and actual creative practices. As technologies advanced, this gap became increasingly large. The tension between technological advancement and legal constraints established the foundation for today\u2019s even more complex legal challenge due to the entrance of AI-generated content into media markets. The legal frameworks developed in response to digital piracy\u2013\u2013including the Digital Millenium Copyright Act (DMCA) and its safe harbor provisions\u2013\u2013now face additional strain.[14]<\/sup><\/a><\/p>\n\n\n\n

II.  AI: Data Collaging and Legal Precedents  <\/strong><\/p>\n\n\n\n

At its core, AI is a collage-making tool that continuously learns from datasets containing various types of media\u2013\u2013including written essays, videos, images, and sounds\u2013\u2013as algorithms piece together responses and conclusions based on user requests and behavior. Any response given to a user by an AI platform such as OpenAI\u2019s ChatGPT is a literary collage that has pulled from\u2013\u2013or, in musical terms, sampled\u2013\u2013specific clauses and ideas from the datasets on which a model has been trained. In other terms, AI uses\u2013\u2013 and potentially pirates\u2013\u2013content from miscellaneous online sites to achieve its designed purpose.<\/p>\n\n\n\n

When AI systems are trained on copyrighted materials without the explicit permission of their authors, as most currently do, they potentially engage in what copyright law classifies as massive-scale appropriation. Unlike human sampling artists who select specific elements with artistic intent, AI systems indiscriminately absorb vast datasets from which they create derivative works with increasingly opaque sourcing. This opacity is compounded by the fact that creative works\u2019 privacy and usage rights are increasingly determined not by traditional legal frameworks but by AI developers\u2019 unilaterally defined terms of service and data policies that often provide minimal transparency regarding training data sources or compensation mechanisms for original creators.[15]<\/sup><\/a> When considering the implications of AI on IP law, it is important to consider the genres, artists, libraries, and assumptions that many of these models are trained on.<\/p>\n\n\n\n

Cases involving sampling\u2013\u2013a legally contentious aspect of contemporary music production in which portions of existing sounds are repurposed in new works\u2013\u2013in the music industry highlight the sources of rigidity and adaptability in IP law, and they offer valuable insight into understanding how IP law can adapt to AI\u2019s data-driven content generation. The landmark case Grand Upright Music Ltd. V. Warner Bros. Records Inc.<\/em> established a zero-tolerance precedent for unauthorized sampling, emphasizing the need to obtain an original creator\u2019s approval even for brief samples.[16]<\/sup><\/a> The U.S. District Court for the Southern District of New York found that unauthorized sampling constitutes copyright infringement, famously beginning its opinion by stating, \u201cThou shalt not steal.\u201d[17]<\/sup><\/a> The court rejected Biz Markie\u2019s unauthorized sampling of Gilbert O\u2019Sullivan\u2019s \u201cAlone Again (Naturally)\u201d and established that even brief, unaltered samples required licensing.[18]<\/sup><\/a> This rigid approach created significant production barriers for hip-hop artists, DJs, and other musicians who relied on sampling as a fundamental creative technique of their era.  Three years later, in 1994, Campbell v. Acuff-Rose Music,<\/em> Inc.<\/em> presented a striking contrast in legal reasoning.[19]<\/sup><\/a> When 2 Live Crew, a rap duo, was sued for copyright infringement over a parody they had created of Roy Orbinson\u2019s \u201cOh, Pretty Woman,\u201d the U.S Court of Appeals for the Sixth Circuit ruled in favor of the duo and found that their sample did not violate copyright law.[20]<\/sup><\/a> The case established the precedent that transformative works such as parodies could, in some circumstances, qualify as fair use even with intense sampling.[21]<\/sup><\/a> Now, experts are struggling to determine whether AI-generated content can be considered a parody of original content as well.<\/p>\n\n\n\n

The contrast between Grand Upright Music Ltd.<\/em>and Campbell <\/em>highlights a significant evolution in sampling laws: a shift from viewing sampling as inherent theft towards viewing it as a potentially transformative form of creative expression. This evolution mirrors the broader cultural and technological shifts of the 1990s, as digital production tools became more accessible and remix culture gained increasing popularity in artistic communities. The legal system began\u2013\u2013albeit inconsistently\u2013\u2013to acknowledge that creativity is a process that often builds upon existing works.<\/p>\n\n\n\n

The legal distinction between intentionality and artificiality has profound implications for AI-generated content. Human creators such as 2 Live Crew intentionally infused their samples with cultural commentary and clear artistic purpose\u2013\u2013elements that algorithms cannot independently replicate. Current AI models lack the ability to discern the type of cultural context and personal experience that constituted fair use in Campbell<\/em> raising questions about whether AI-generated content could ever truly be transformative in the legal sense without significant human intervention.<\/p>\n\n\n\n

While these cases established important legal precedents, technological innovations continued to outpace the law\u2019s capacity to effectively regulate creative industries and enforce traditional copyright protections. International agreements such as the 1996 World Intellectual Property Organization (WIPO) Copyright Treaty attempted to mitigate this issue by directly addressing \u201ccopyright and related rights in the Internet age\u201d via specific provisions regulating digital transmission rights and protecting computer programs and databases.[22]<\/sup><\/a> However, this top-down approach proved insufficient against the decentralized nature of digital creative industries and the rapid pace of technological change.<\/p>\n\n\n\n

Artists and other industry professionals developed pragmatic alternatives when faced with this gap between legal frameworks and creative realities. Collective management organizations such as the American Society of Composers, Authors, and Publishers and Broadcast Music, Inc. emerged as crucial intermediaries that simplified the licensing process by allowing creators to license works through a single entity rather than requiring the negotiation of several individual agreements. These organizations helped artists navigate increasingly complex copyright landscapes while ensuring they received appropriate compensation.[23]<\/sup><\/a> Similarly, Creative Commons licensing offered a flexible alternative to traditional copyright, allowing creators to specify permissible uses of their work with options ranging from \u201call rights reserved\u201d to various controlled levels of sharing and remixing. These systems emerged specifically because traditional copyright frameworks could not efficiently or effectively handle the increasing volume and speed of digital creative exchange. They also indicate a historical trend in IP law: when formal legal systems fail to accommodate the evolving needs of creatives, stakeholders develop parallel legal structures that better serve their needs. This pattern has critical implications for AI regulation, suggesting that overly rigid legal approach may drive innovation into less regulated spaces. Thus, flexible frameworks that acknowledge the evolving technological realities of creative industries may prove more effective. <\/p>\n\n\n\n

III. Originality<\/strong><\/p>\n\n\n\n

In copyright law, originality has specific legal significance that directly impacts the evaluation of sampling and AI-generated content. The Supreme Court set this standard in Feist Publications v. Rural Telephone Service<\/em>, establishing that copyright protection requires a \u201cmodicum of creativity\u201d and that works must be \u201cindependently created\u201d rather than copied.[24]<\/sup><\/a> This standard explicitly rejected the \u201csweat of the brow\u201d doctrine, meaning that effort alone could not confer copyright protection without a meaningful creative contribution.[25]<\/sup><\/a><\/p>\n\n\n\n

Regarding music sampling, the question of originality occurs at two levels. First is whether or not the original work meets the creativity threshold, which is almost always the case. Second is whether the new work incorporating samples of the original work demonstrates sufficient originality itself to constitute independent production. AI-generated content adds a new layer of complexity to these questions, as it is debated whether models trained on copyrighted works can meet the \u201cindependently created\u201d requirement. Current legal frameworks rely heavily on originality, as upheld in Kelly v. Arriba Soft Corp<\/em>.[26]<\/sup><\/a> In this case, photographer Leslie Kelly sued Arriba Soft Corporation, an internet search engine, for displaying thumbnail images of his copyrighted photographs in its search engine without his authorization.[27]<\/sup><\/a> The Ninth Circuit held that Arriba\u2019s use of the thumbnails constituted fair use, emphasizing the transformative nature of the use, as the thumbnails served a new purpose: they were a search tool rather than a form of artistic expression.[28]<\/sup><\/a> The court also noted that using low-resolution thumbnails minimized any potential economic harm to Kelly, as they would be unlikely to adequately substitute for the original images.[29]<\/a> Specifically, the court distinguished between different forms of images\u2013\u2013 thumbnails and full-sized original photos\u2013\u2013demonstrating a nuanced approach to digital reproduction.[30]<\/sup><\/a> This case offers a direct parallel to exploring AI\u2019s impact on IP.<\/p>\n\n\n\n

A more recent case from 2020, Skidmore v. Led Zeppelin<\/em>, emphasizes the importance of originality in protecting musical works.[31]<\/sup><\/a> The central issue in question was whether certain songs were similar enough to constitute copyright infringement.[32]<\/sup><\/a> The Ninth Circuit determined that while there were similarities between two riffs, they were not sufficiently similar to warrant a claim of infringement.[33]<\/a> Regarding AI-generated works, this case serves as a potential precedent for considering original works repurposed by entities other than the original author. <\/p>\n\n\n\n

The recursive nature of AI\u2013\u2013where outputs can inform future inputs\u2013\u2013creates a dynamic that renders previous legal precedents increasingly ambiguous, demanding a more sophisticated legal framework. Generative AI, in particular, introduced a novel dimension to these debates and blurred the lines between inspiration, reproduction, and originality. For instance, if a large language model (LLM) produced song lyrics based on user prompts, does the user, the LLM developer, or neither hold rights to the output? Is it collectively owned by the creators of the works used to train a given algorithm? Can AI\u2013\u2013and other nonhuman entities\u2013\u2013have rights themselves? The rise of global digital access only compounded this issue. As WIPO noted, significant issues remained unresolved, particularly regarding ensuring that \u201cmusicians and artists receive proper payment when their works can be accessed online anywhere in the world,\u201d a problem exemplified by the global nature of digital content distribution and the limitations of existing territorial copyright frameworks.[34]<\/sup><\/a><\/p>\n\n\n\n

These ambiguities expose further gaps in current laws, as LLMs trained on copyrighted music or lyrics without an original author\u2019s permission may violate Section 1201 of the DMCA, which prohibits the circumvention of technological measures designed to protect copyrighted works.[35]<\/sup><\/a> The DMCA and similar copyright laws face new challenges due to AI\u2019s ability to generate works that mimic those created by humans. This requires a fundamental reimagining of IP law that moves beyond binary distinctions of authorship and recognizes the complex, multi-layered nature of contemporary content creation. Without such adaptations, copyright laws risk becoming ineffective.<\/p>\n\n\n\n

IV. Open-Source Collaboration<\/strong><\/p>\n\n\n\n

As AI technologies grow more accessible, capable, and autonomous, the lines between AI-generated and human-produced music are blurred, challenging legal frameworks initially crafted to protect human creations. Even modern legislation focusing on digital works has failed to account for the nuances introduced by AI, exposing the limitations of existing copyright law in protecting and categorizing hybrid human-machine creations.<\/p>\n\n\n\n

A. Foundational Digital Copyright Acts<\/strong><\/p>\n\n\n\n

The DMCA established a legal framework for protecting digital works by restricting access to pirated content.[36]<\/sup><\/a> While the DMCA successfully curtails piracy, its rigid mechanisms, such as takedown notices, have been criticized by artists, scholars, and digital rights advocates for stifling fair use, parody, and remixing\u2013\u2013all practices central to modern music production and digital culture. It has also been criticized because it fails to safeguard original creative content from being used as \u201cinspiration\u201d for new works such as remixes or AI-generated variations. <\/sup>Similarly, the 2018 Copyright Modernization Act\u2013\u2013more commonly known as the Music Modernization Act (MMA)\u2013\u2013was designed to improve financial and legal clarity in the music industry.[37]<\/a> It is intended to ensure proper credit and compensation for all contributors to a piece of music and to address licensing challenges faced by digital streaming platforms.[38]<\/sup><\/a> However, the MMA\u2013\u2013like the DMCA\u2013\u2013fails to contend with the legal and ethical questions raised by the increasing prevalence of AI-generated content, and music specifically, in creative industries.<\/p>\n\n\n\n

Both the DMCA and the MMA reveal a fundamental tension in copyright law: existing legal frameworks struggle to categorize and protect creative works that emerge from hybrid human-AI collaborations. These acts\u2019 provisions assume that all creative works emerge from clear, identifiable authorship, a paradigm that is increasingly at odds with AI-generated music that blurs the boundaries between a creator and their tools. This blurred boundary raises questions about credit, ownership, and value distribution that neither the DMCA nor the MMA can fully revolve.<\/p>\n\n\n\n

B. Key Court Decisions<\/strong><\/p>\n\n\n\n

The evolving dynamics of human-AI collaboration increasingly intersect with longstanding legal debates over transformative use, authorship, and platform liability. A series of key court decisions illustrates both the legal precedents shaping this terrain and the growing inadequacy of traditional frameworks for addressing the hybridized nature of AI-generated content.<\/p>\n\n\n\n

In Authors Guild v. Google, Inc.<\/em> (2015), several courts assessed whether the use of copyrighted material for transformative purposes was permissible, a question relevant to AI models that are trained on data derived from existing copyrighted music.[39]<\/sup><\/a> Ultimately, the Second Circuit found that Google\u2019s digitization and display of small excerpts of books for search purposes was transformative, did not substitute for the original works, and did not significantly harm the market.[40]<\/sup><\/a> While this ruling focused on written material, its reasoning provides a foundational lens through which to view the training of AI models: using existing works to develop new technological tools may, under certain conditions, qualify as fair use. However, this presumes that the outputs serve a fundamentally different purpose than the training data, a presumption that is not always clear with generative AI that mimics original works.<\/p>\n\n\n\n

In Andy Warhol Foundation for the Visual Arts, Inc. v. Goldsmith,<\/em> the Supreme Court clarified what constituted a transformative use, a key issue for AI systems that remix or reinterpret existing musical pieces.[41]<\/sup><\/a> The Court found that Warhol\u2019s silkscreen image based on Goldsmith\u2019s photograph of Prince was not fair use, citing the commercial nature of the reuse and insufficient transformative content.[42]<\/sup><\/a> This decision indicates a tightening of fair use standards, emphasizing that courts must rigorously assess whether or not a new work adds new meaning to an original work, an ambiguous test when applied to generative AI systems that output variations of existing media with unclear degrees of human intervention and creative intent.<\/p>\n\n\n\n

BMG Rights Management (U.S.) LLC v. Cox Communications, Inc<\/em>. concerned digital platforms\u2019 liability for hosting content that infringed on a copyright, a growing concern as AI-generated works proliferate.[43]<\/sup><\/a> In this case, the Fourth Circuit ruled that Cox Communications could not claim safe harbor under the DMCA because it knowingly failed to terminate repeat infringers.[44]<\/sup><\/a> The court\u2019s decision signaled that service providers could bear legal responsibility if and when they profit from copyright infringement, and it also indicated that platforms were not merely conduits but also actors responsible for complying with copyright laws.<\/p>\n\n\n\n

In a landmark decision concerning AI-generated content, Thaler v. Perlmutter<\/em> affirmed that works created without human authorship cannot not be copyrighted.[45]<\/sup><\/a> When Thaler sued the U.S. Copyright Office claiming that copyright law should be interpreted to allow AI systems to be recognized as authors, the District Court for the District of Columbia upheld the Copyright Office\u2019s decision to only recognize human authors, reinforcing the traditional view of authorship.[46]<\/sup><\/a> This case presents a clear legal boundary that excludes autonomous AI-generated works from copyright protections. In practice, this means that creators using AI must demonstrate meaningful human contribution to receive copyright protection, although what constitutes human contribution remains unclear.<\/p>\n\n\n\n

Existing frameworks such as the DMCA and the MMA cannot answer such complex questions, as they often default to binary distinctions of authorship that fail to recognize the hybrid nature of AI-assisted creative content. Taken together, the aforementioned court decisions demonstrate a legal system grappling with contradictions. Courts reward innovation under the fair use doctrine in some contexts but restrict it when they perceive commercial exploitation or insufficient transformative content. They demand human authorship while technological advancements increasingly defy traditional conceptions of authorship. They impose liability on platforms without offering clear standards for when algorithmic or user-generated actions cross legal lines. The central tension here is that IP law presumes clear boundaries between authors and creative tools, while AI collapses these binaries. Moving forward, legal reforms must explicitly address the gray area of human-AI collaborative authorship. Without such clarity, creators, platforms, and copyright holders will remain mired in uncertainty and the very innovation that copyright law is meant to promote will be stifled.<\/p>\n\n\n\n

C. The Question of Human Authorship<\/strong><\/p>\n\n\n\n

Community for Creative Non-Violence v. Reid <\/em>offers a framework for understanding the question of who owns AI-assisted creations in a \u201cwork made for hire\u201d context: the artist, the label, or neither?[47]<\/sup><\/a> When CCNV commissioned sculptor James Earl Reid to design a statue portraying homelessness, both CCNV and Reid claimed copyright ownership of the final product titled Third World America.[48]<\/sup><\/a> The Supreme Court was tasked with determining whether Reid was an independent contractor or an employee of CCNV, as copyright ownership of works created under a \u201cwork made for hire\u201d arrangement depends on this important distinction.[49]<\/sup><\/a> The Court ultimately ruled that, because CCNV did not have control over the manner and means of his work, Reid was an independent contractor and was granted copyright ownership of Third World America. This decision established a multifactor test based on common law of agency principles to determine employment status for copyright purposes, significantly influencing how copyright ownership is determined in collaborative creative efforts.<\/p>\n\n\n\n

Over a decade later, Morris v. The Business Journal Publications, Inc.<\/em> underscored the challenges of determining authorship when multiple contributors are involved, a scenario that becomes even more complex with AI.[50]<\/sup><\/a> In this case, Diane Morris, a journalist, sued The Business Journal Publications, Inc. for using her lists of executive compensation figures without permission.[51]<\/sup><\/a> The Second Circuit affirmed the principle established in Feist Publications, Inc. v. Rural Telephone Service Co.<\/em>, holding that while the arrangement of factual data can be copyrighted if it reflects original selection or coordination, the mere presentation of facts without creative input does not meet the threshold for copyright protection.[52]<\/sup><\/a> These early cases established the critical criteria for copyright protection: human authorship, originality, and creative input. Their precedents directly challenge AI\u2019s current copyright status, highlighting the requirements of human intentionality and creative selection that AI systems struggle to demonstrate. As authorship becomes increasingly collaborative between humans and AI systems, new frameworks are needed to address the murky intersection of creativity, labor, and machine influence in contemporary music production.<\/p>\n\n\n\n

D. Sampling and Remixing<\/strong><\/p>\n\n\n\n

In 2005, Bridgeport Music, Inc. v. Dimension Films <\/em>introduced the \u201cget a license or do not sample\u201d standard, which limits artists\u2019 freedom to reuse and remix sounds.[53]<\/sup><\/a>[54]<\/sup><\/a> Bridgeport Music, the owner of the copyright to a song by Funkadelic, sued Dimension Films over a three-note guitar riff that was sampled with permission in the N.W.A.\u2019s song 100 Miles and Runnin\u2019, which was featured in a Dimension Films Movie.[55]<\/sup><\/a> The Sixth Circuit held that any unauthorized digital sampling, even if it is brief or unrecognizable, constitutes copyright infringement.55<\/sup> The court rejected Dimension Films\u2019 de minimis defense, stating that musicians must \u201cget a license or [] not sample.\u201d[56]<\/a> The ruling sparked significant debate in the music industry, as critics argued that it stifled creativity and introduced barriers for artists, particularly in genres such as hip-hop, where sampling is a foundational creative technique. This precedent highlights the tension between traditional copyright protections and the inherently iterative nature of human and AI-generated works, which often synthesize preexisting material without explicit permission or licensing.<\/p>\n\n\n\n

E. Creative Labor and Dynamic Authorship<\/strong><\/p>\n\n\n\n

AI\u2019s ability to skip various production stages introduces efficiencies to the creative process but challenges the creative interventions that traditionally shaped each stage. AI-generated works risk significantly affecting the results of the process by introducing new creative dynamics that erode the diversity and authenticity that IP law strives to protect. When an artist uses AI to generate melodies, harmonies, or entire arrangement, they are using it both as a tool and a collaborator. While not inherently revolutionary, this approach is worth considering for its potential to expand creative workflows and enable innovation in music production. Procedural industry standards serve as ethical guardrails, promoting fair compensation and respect for creative labor. Balancing these competing interests and reconciling past issues are two of the biggest challenges facing lawmakers and industry leaders today, as they must ensure that AI\u2019s beneficial contributions are leveraged without compromising human artists\u2019 rights, integrity, and livelihood. The economic implications of this symbiosis are particularly dramatic, creating a potential power redistribution between artists and record labels. By democratizing production tools, AI empowers independent creators to experiment and produce music with fewer resources. This democratization, while exciting, introduces legal and financial challenges, such as redefining revenue-sharing models and adjusting royalty frameworks to accommodate AI\u2019s contributions.             <\/p>\n\n\n\n

Aalmuhammed v. Lee<\/em> (2000) addresses an interesting and related question of joint authorship.[57]<\/sup><\/a> Jefri Aalmuhammed, a consultant and advisor on one of Spike Lee\u2019s films, claimed that his contributions to the production\u2013\u2013including the writing of dialogue and script editing\u2013\u2013made him a co-author of the film and entitled him to joint copyright ownership.[58]<\/sup><\/a> Lee and Warner Bros. argued that his contributions were merely advisory and did not warrant authorship status.[59]<\/a> In its decision, the Ninth Circuit ruled in favor of Lee, finding that Aalmuhammed\u2019s role was insufficient to establish co-authorship because his contributions were did not constitute significant creative input.[60]<\/a> This case provides a strong foundation for imagining how artists may be treated as authors when they incorporate AI-generated content in their music. Future IP laws must balance protecting human creativity and acknowledge AI\u2019s role without undermining the economic rights of artists whose work is used to train AI models. This will require revisiting the question of authorship to account for varying degrees of human input. The traditional music ecosystem as AI continues to evolve, forcing a reevaluation of how creative value is measured and compensated.<\/p>\n\n\n\n

V. Proposed Framework for the Future<\/strong><\/p>\n\n\n\n

IP law must evolve to reflect the new legal realities of the creative landscape in which there is hybrid authorship between humans and AI systems. The future of copyright law must protect humans\u2019 creative labor and facilitate the transformative and innovative uses of AI. Foundational music licensing cases and digital rights laws offer egal scaffolding for such a transition, but they must be reinterpreted and expanded to meet the demands of AI. <\/p>\n\n\n\n

A. Reconceptualizing Authorship as a Spectrum<\/strong><\/p>\n\n\n\n

Traditional copyright law hinges on human originality. Yet, in AI-assisted creation, human involvement varies from passive prompting to fine-grained direction. Future IP frameworks should recognize authorship as a spectrum rather than a binary. A potential system could offer a tiered system of protection. Works with substantial human input would receive full copyright; works with moderate human input would receive limited protection on shorter terms or with compulsory licensing; and works with minimal or no human authorship would receive modified public domain or Creative Commons-style access. This approach would build upon Aalmuhammed v. Lee<\/em>, which addressed joint authorship and the weight of creative contributions.[61]<\/sup><\/a><\/p>\n\n\n\n

B. Transparency and Attribution Requirements for AI Developers and Data Scientists<\/strong><\/p>\n\n\n\n

AI systems must maintain transparent logs of training data to allow for proper attribution and compensation to original creators, and developers should create standardized compensation mechanisms for creators whose works influence AI-generated content. Regulators could impose transparency requirements as part of an AI system\u2019s eligibility for commercial deployment by implementing dataset registries similar to the sample clearance databases used in music licensing. Additionally, creating attribution tokens or metadata trails tied to specific AI outputs would be helpful. Drawing from Bridgeport Music v. Dimension Films<\/em>, where sampling\u2013\u2013even in small amounts\u2013\u2013required licensing, this framework avoids binary \u201cget a license or do not sample\u201d requirements and instead promotes documented influence with proper compensation for creators throughout the entire line of creative content production.<\/p>\n\n\n\n

C. The Fair Use Doctrine and AI-specific Metrics         <\/strong><\/p>\n\n\n\n

While Campbell v. Acuff-Rose Music<\/em> affirmed that parody and transformation can constitute fair use even when they are used for commercial purposes, courts have lacked a consistent way to evaluate transformation in machine-generated works.[62]<\/sup><\/a> Rather, they must consider several questions to reach nuanced conclusions on a case-by-case basis. Does the AI output merely replicate the original style, or does it also build upon it? Is the purpose of the new content experimental, artistic, derivative, or commercial? How much influence did the borrowed material have on the output? These questions allow courts to differentiate between AI tools used for creative expansion, such as creating a new style of jazz, as opposed to those used solely to mimic and profit from existing work.<\/p>\n\n\n\n

D. Collective Licensing Systems and Algorithmic Royalties         <\/strong><\/p>\n\n\n\n

The introduction of AI-generated content to the music industry has necessitated the creation of a new and innovative compensation mechanism. AI-specific licensing frameworks must enable creative use while ensuring fair compensation, including blanket licensing for AI training and algorithmic royalty distribution. Mechanical Licensing Collective (MLC) is a model that can be applied to cases involving the use of generative AI, which requires a scalable, inclusive, and automatic compensation system.<\/p>\n\n\n\n

E. Regulatory Oversight and Technical Standards<\/strong><\/p>\n\n\n\n

Policies are toothless without enforceable norms, so governance must be grounded in technical feasibility. U.S. regulatory bodies should establish similar standards for transparency, attribution, ethical uses of training data, and fingerprinting technologies. Specifically, industry stakeholders should require content fingerprinting or watermarking for both training data and AI model outputs; mandate interoperable metadata formats for tracking the lineage of AI-generated content; establish third-party audit protocols to verify dataset legality and compensation flows; and empower regulatory bodies such as the Copyright Office and the FTC to oversee compliance and publish transparent reports.<\/p>\n\n\n\n

F. Cultural and Educational Infrastructure<\/strong><\/p>\n\n\n\n

Legal reform alone will not shift the music industry\u2019s norms. A holistic response must also empower creators with legal literacy and technical fluency. Addressing AI\u2019s impact requires cultural and educational initiatives that will prepare creators to work with AI tools while allowing them to understand the legal implications of doing so. The history of sampling culture and related legal cases shows that restrictive approaches such as Bridgeport Music\u2019s<\/em> \u201cget a license or do not sample\u201d standard can stifle innovation and disproportionately affect marginalized communities. Thus, education is a key part of an equitable future for IP law.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

AI represents not only a technological disruption to the music industry, but also a philosophical shift in how we understand creativity, labor, and value. While Prince fought decades ago for more artistic control over his work and his brand, the challenge facing artists today is less about personal ownership and more about grappling with the increasing entanglement of humans and machines. Moving forward, legal experts and industry professionals must reject rigid binaries in favor of flexible and modular systems that protect human creators, reward transparency and cultural stewardship, clarify the role of AI in creative processes, and sustain innovation. By building legal, technical, and cultural infrastructures in tandem, we can create an ecosystem in which human and AI creators can coexist symbiotically rather than competitively.<\/p>\n\n\n\n

\n\n\n\n

[1]<\/a> See<\/em> Eamonn Forde, Record Breaker: A Brief History of Prince\u2019s Contractual Controversies<\/em>, The Guardian, Aug. 10, 2015, https:\/\/www.theguardian. com\/music\/2015\/aug\/10\/history-prince-contractual-controversy-warner-paisley-park.<\/p>\n\n\n\n

[2]<\/a> Id. <\/em>  <\/p>\n\n\n\n

[3]<\/a> World Intellectual Property Organization, What Is Intellectual Property? (2020), https:\/\/www.wipo.int\/edocs\/pubdocs\/en\/wipo_pub_450_2020.pdf.<\/p>\n\n\n\n

[4]<\/a> Id<\/em>. at 23.<\/p>\n\n\n\n

[5]<\/a> Michael L. Rustad & Diane D\u2019Angelo, The Path of Internet Law: An Annotated Guide to Legal Landmarks<\/em>, 10 Duke Law Technol. Rev. 1, 21<\/p>\n\n\n\n

(2011).<\/p>\n\n\n\n

[6]<\/a> Manish Jindal, History of Piracy \u2013 How Did It Emerge?<\/em>, Bytescare, Sep. 3, 2024, http:\/\/bytescare.com\/blog\/history-of-piracy.<\/p>\n\n\n\n

[7]<\/a> Id. <\/em> <\/p>\n\n\n\n

[8]<\/a> A&M Records, Inc. v. Napster, Inc., 239 F.3d 1004 (9th Cir. 2001).<\/p>\n\n\n\n

[9]<\/a> Id.<\/em><\/p>\n\n\n\n

[10]<\/a> Id.            <\/em> <\/p>\n\n\n\n

[11]<\/a> MGM Studios, Inc. v. Grokster, Ltd., 545 U.S. 913 (2005). 12     UMG Recordings, Inc. v. MP3.com, Inc., 92 F. Supp. 2d 349, 350\u201352 (S.D.N.Y. 2000).<\/p>\n\n\n\n

[12]<\/a> UMG Recordings, Inc. v. MP3.com, Inc., 92 F. Supp. 2d 349, 350\u201352 (S.D.N.Y. 2000).<\/p>\n\n\n\n

[13]<\/a> Id.<\/em><\/p>\n\n\n\n

[14]<\/a> Digital Millennium Copyright Act<\/em>, Pub. L. No. 105-304, 112 Stat. 2860 (1998). (Enacted in 1998, the DMCA implements two international treaties adopted by the World Intellectual Property Organization (WIPO) and introduces provisions to combat online copyright infringement. Primarily designed to implement two 1996 World Intellectual Property Organization (WIPO) treaties, the DMCA addressed several critical issues in digital copyright protection. Most notably, it introduced safe harbor provisions (17 U.S.C. \u00a7 512) that shield internet service providers from liability for copyright infringement committed by their users, provided they promptly remove infringing content upon receiving a takedown notice.) <\/p>\n\n\n\n

[15]<\/a> See <\/em>Eryk Salvaggio, Context, Consent, and Control: The Three C\u2019s of Data Participation in the Age of AI<\/em>, Tech Policy Press (June 12, 2024).<\/p>\n\n\n\n

[16]<\/a> Grand Upright Music Ltd. v. Warner Bro\u2019s Records, Inc., 780 F. Supp. 182 (S.D.N.Y. 1991)<\/p>\n\n\n\n

[17]<\/a> Id.                                                                                                                                     <\/em> <\/p>\n\n\n\n

[18]<\/a> Id.<\/em><\/p>\n\n\n\n

[19]<\/a> Campbell v. Acuff-Rose Music, Inc., 510 U.S. 569 (1994).<\/p>\n\n\n\n

[20]<\/a> Id.<\/em><\/p>\n\n\n\n

[21]<\/a> Id.<\/em><\/p>\n\n\n\n

[22]<\/a> World Intellectual Property Organization, WIPO Copyright Treaty<\/em> (WCT)<\/em>, WIPO, https:\/\/www.wipo.int\/treaties\/en\/ip\/wct\/<\/p>\n\n\n\n

[23]<\/a> World Intellectual Property Organization, Collective Management of Copyright and Related Rights<\/em>, WIPO, 2d ed. 2009.<\/p>\n\n\n\n

[24]<\/a>Feist Publ\u2019ns, Inc. v. Rural Tel. Serv. Co., 499 U.S. 340, 345\u201351 (1991).                      <\/p>\n\n\n\n

[25]<\/a>Id.<\/em><\/p>\n\n\n\n

[26]<\/a> Kelly v. Arriba Soft Corp., 336 F.3d 811, 818\u201322 (9th Cir. 2003).<\/p>\n\n\n\n

[27]<\/a> Id.                                                                                                                                     <\/em> <\/p>\n\n\n\n

[28]<\/a> Id.<\/em><\/p>\n\n\n\n

[29]<\/a> Id.<\/em><\/p>\n\n\n\n

[30]<\/a> Id.<\/em><\/p>\n\n\n\n

[31]<\/a> Skidmore v. Led Zeppelin, 952 F.3d 1051 (9th Cir. 2020).<\/p>\n\n\n\n

[32]<\/a> Id.<\/em><\/p>\n\n\n\n

[33]<\/a> Id.<\/em><\/p>\n\n\n\n

[34]<\/a> World Intellectual Property Organization, supra <\/em>note 3.<\/p>\n\n\n\n

[35]<\/a> Digital Millennium Copyright Act, 17 U.S.C. \u00a7 1201 (2020).<\/p>\n\n\n\n

[36]<\/a> Id.<\/em><\/p>\n\n\n\n

[37]<\/a> Orrin G. Hatch\u2013Bob Goodlatte Music Modernization Act, Pub. L. No. 115-264, 132 Stat. 3676 (2018).<\/p>\n\n\n\n

[38]<\/a> Id.<\/em><\/p>\n\n\n\n

[39]<\/a>                                                                                           Authors Guild v. Google, Inc., 804 F.3d 202 (2d Cir. 2015). <\/p>\n\n\n\n

[40]<\/a> Id.<\/em><\/p>\n\n\n\n

[41]<\/a> Andy Warhol Foundation v. Goldsmith, 143 S. Ct. 1258 (2023).<\/p>\n\n\n\n

[42]<\/a> Id.<\/em><\/p>\n\n\n\n

[43]<\/a> BMG Rights Management (U.S.) LLC v. Cox Communications, Inc., 881 F.3d 293 (4th Cir. 2018).<\/p>\n\n\n\n

[44]<\/a> Id.<\/em><\/p>\n\n\n\n

[45]<\/a> Thaler v. Perlmutter, No. 1:22-cv-01564 (D.D.C. 2023).<\/p>\n\n\n\n

[46]<\/a> Id.<\/em><\/p>\n\n\n\n

[47]<\/a> Community for Creative Non-Violence v. Reid, 490 U.S. 730<\/p>\n\n\n\n

(1989). <\/p>\n\n\n\n

[48]<\/a> Id.<\/em><\/p>\n\n\n\n

[49]<\/a> Id.<\/em><\/p>\n\n\n\n

[50]<\/a> Morris v. The Bus. Journal Publ\u2019ns, Inc., 259 F.3d 1167, 1172\u201375 (9th Cir. 2001) (ruling that the use of a copyrighted photograph in an online publication without permission was a violation of copyright law, despite the defendant\u2019s argument of fair use).<\/p>\n\n\n\n

[51]<\/a> Id.<\/em><\/p>\n\n\n\n

[52]<\/a> Id.<\/em><\/p>\n\n\n\n

[53]<\/a> Bridgeport Music, Inc. v. Dimension Films, 410 F.3d 797 (6th Cir. 2005). (Bridgeport Music, Inc. v. Dimension Films, 410 F.3d 797, 804\u201308 (6th<\/sup> Cir. 2005) (holding that the unauthorized sampling of a copyrighted musical composition, even if the sample was a minimal amount, constituted infringement<\/p>\n\n\n\n

and rejecting the de minimis defense in copyright cases).<\/p>\n\n\n\n

[54]<\/a> Id.<\/em><\/p>\n\n\n\n

[55]<\/a> Id.<\/em><\/p>\n\n\n\n

[56]<\/a> Id.<\/em><\/p>\n\n\n\n

[57]<\/a> Aalmuhammed v. Lee, 202 F.3d 1227, 1232\u201335 (Fed. Cir. 2000).<\/p>\n\n\n\n

[58]<\/a> Id.<\/em><\/p>\n\n\n\n

[59]<\/a> Id.<\/em><\/p>\n\n\n\n

[60]<\/a> Id.<\/em><\/p>\n\n\n\n

[61]<\/a> Aalmuhammed v. Lee, 202 F.3d 1227, 1232\u201335 (Fed. Cir. 2000).<\/p>\n\n\n\n

[62]<\/a> Campbell v. Acuff-Rose Music, Inc., 510 U.S. 569 (1994).<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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