From policy to practice: The music industry’s AI ethics playbook
The music industry's approach to AI ethics has evolved from academic discussions to business frameworks that are already influencing deals and partnerships. Some major platforms, such as YouTube and Deezer, are aligning with rights holders’ demands; many others face litigation.
For both tech companies and music professionals, understanding these ethical frameworks has become crucial for doing business in AI. The music industry's unified voice has grown stronger: Between 2023 and 2024, over 400 music industry organizations published or co-signed nearly 20 ethics statements and rights declarations, asserting a range of positions on AI training, copyright, and fair compensation.
Let's break down where the industry has found common ground — and where key questions remain open.
Tier 1: Strongest industry consensus
Two fundamental principles have emerged as non-negotiable requirements for AI companies seeking music industry partnerships:
Licensing & control
The industry's position on AI training has evolved from general rights protection to explicit licensing frameworks, with major labels leading the charge through increasingly specific demands.
Warner Music Group's January 2024 statement captures this shift: "The use of our copyrighted music and our artists' NILV [name, image, likeness, and voice] rights to train AI engines and to create output from those models should require a free-market license."
By May 2024, Sony Music went further with a formal "Declaration of AI Training Opt Out," explicitly prohibiting any unauthorized AI training on their content and establishing a dedicated email address for AI licensing inquiries.
Similarly, Merlin's December 2024 policy requires "specific and express license, in advance" for any AI training — effectively closing the gap between major and independent approaches.
The tech platform response
While the music industry has united around clear licensing requirements, most AI developers have yet to publicly engage with these frameworks.
The implications for developers are significant: Comprehensive documentation of training data sources, mandatory licensing before any ingestion of copyrighted works, transparent record-keeping, and market-rate compensation expectations for data can all be costly and slow down development. It’s no surprise that many consumer-facing audio and text AI tools, including Claude (owned by Anthropic), Suno, and Udio, are locked in legal battles with music rights holders.
However, there are a few exceptions:
- YouTube stands out as the first major music consumption platform to proactively align with industry principles on AI. Its August 2023 framework, developed with Universal Music Group, explicitly acknowledges the need for proper licensing and creator control, implemented in experimental AI tools like Dream Track.
- Deezer became one of the only streaming services to endorse the Statement on AI Training in October 2024, asserting that “the unlicensed use of creative works for training generative AI is a major, unjust threat to the livelihoods of the people behind those works, and must not be permitted.”
- Roland partnered with UMG to establish industry-standard principles that have since been endorsed by 50 other music tech brands, including Splice, Native Instruments, Focusrite, and Output.
Protecting human creativity
The Human Artistry Campaign's March 2023 principles set a crucial benchmark, establishing that "copyright should only protect the unique value of human intellectual creativity." Standout points in the campaign emphasize that “art cannot exist independent of human culture,” and that copyright protection exists to “reward human creativity, skill, labor, and judgment” — not output solely created by machines.
These principles have since gained traction and evolved into specific criteria across the industry. For instance:
- WMG’s 2024 statement echoes this position, declaring that “there is nothing more precious than our artists’ voice and protecting the livelihood and persona of our artists and songwriters,” backed by specific protections for NILV rights.
- GEMA's November 2024 AI Charter takes a “digital humanism” approach, where “the development of generative AI is obligated to the well-being of people.”
- WIN’s May 2024 principles go further, stating not only that “facilitating human creativity must remain central to GenAI development, regulation and legal frameworks,” but also that “content generated by AI without human creativity cannot qualify for copyright.”
The consistency across these declarations reveals a unified industry position: AI can enhance human creativity, but cannot replace it.
Tier 2: Evolving standards and debates
As the industry gains practical experience with AI, we’re seeing gradual consensus building around principles that still have room to develop into broader standards:
Transparency & documentation
With AI development accelerating, the industry has progressed from vague calls for transparency to specific documentation requirements.
Record-keeping of training data
- WMG demands that AI companies keep "sufficiently detailed records of the copyrighted music and NILV rights used to train their models and to create output from those models to enable us to enforce our rights."
- Merlin requires a complete "auditable record of the music ingested by AI applications in the machine training process."
- IMPF says record-keeping is "not only socially responsible but will also provide certainty that the use of the specific music has been permitted."
- WIN claims that “record keeping, reporting and auditability of all data used to develop and operate GenAI systems must be legal obligations.”
- UK Music demands that, “in the input stage, the tech providers keep an auditable record of the music ingested before the algorithm generates new music.”
Content identification
Multiple stakeholders demand clear labeling of AI-generated content, often in the name of protecting consumers and creating an equal-opportunity market. WIN claims that “any content generated with full or partial AI input needs clear labeling to promote public trust and informed consumer choice,” while IMPF calls labeling “a fundamental principle of consumer protection.”
However, as our previous Water & Music research reveals, these demands face significant technical hurdles. Current AI detection tools claim high accuracy, yet they struggle to keep pace with rapidly evolving AI models, and can’t always identify which specific parts of a song (e.g. vocals, beats, instruments) use AI. This especially complicates matters for hybrid workflows, where AI is just one part of a larger creative process, e.g. built-in AI features within traditional DAWs like Logic Pro or BandLab.
The gap between industry demands and technical feasibility will require more nuanced frameworks that account for varying degrees of AI involvement, practical implementation costs, and impact on legitimate creative workflows.
Fair value distribution
The industry agrees that creators deserve compensation for AI training data, but the specifics of how to distribute that value reveal some underlying tensions.
Many stakeholders strongly favor letting the market determine fair rates for AI training data. Two illustrative proposals:
- From GEMA: “All parties involved in the value chain receive a fair share of the revenues … A fair remuneration model must start at the point where value is created. As a consequence, it must not just be limited to train up the AI model. On the contrary, the economic advantages must be considered which arise through AI content being generated (e.g. income from subscriptions) and are achieved in the market through ensuing exploitation (e.g. as background music or AI generated music on music platforms on the internet).”
- From FIM: “Innovative remuneration mechanisms based on the output should, therefore, be considered. Any AI-assisted generation of musical content should be subject to fair payments to performers as their work and talent constitute the knowledge base at the origin of such content. Such fair payments, however, must not operate to normalise or unduly encourage the supplanting by generative AI of the work of individual human beings. We need a payment system that will honestly compel a producer who is contemplating the use of generative AI to weigh the economic advantages of human-produced products and performances against the convenience of generative AI-produced products.”
This preference for market-based negotiation is not surprising, given the music industry’s historical preference for direct deals in the face of new tech.
Still, recent research shows that seemingly small design choices in any “pay-for-data” framework can have massive unintended consequences — underscoring how difficult it is to define “fairness” in paying people for their data, especially if large corporations dominate AI training.
For instance, if the industry adopts a per-stream or per-song approach to AI licensing or royalties, certain catalogs could dominate the lion’s share of payouts, depending on which works are trained on most frequently and how deals are structured. Some guardrails, such as minimum payouts or caps, might be necessary to prevent smaller rightsholders from being disadvantaged.
What’s next?
As we move through 2025, the music industry’s focus is shifting from establishing AI principles to implementing them in real-world contexts.
Rights holders are developing standardized licensing frameworks that can scale with AI’s rapid progress, building the technical infrastructure to monitor AI training usage, and shaping market-based compensation models before regulators potentially intervene. Initiatives such as the Dataset Providers Alliance (whose audio members include Rightsify, Pro Sound Effects, and Sound Ideas), SoundExchange’s upcoming AI registry, and ASCAP and SACEM’s new AI task force are creating potential pathways for broader industry cooperation.
For AI developers and platforms, the path to market has become more defined, but also more demanding. Documentation and transparency requirements are now table stakes, while licensing costs must be factored into development budgets from the start. Yet, as YouTube has shown, proactively partnering with rights holders can also serve as a critical differentiator.
Music creators and their teams face a more nuanced landscape, where understanding which rights can be protected versus licensed has become crucial to career strategy. While new revenue streams from training data are emerging, creators must evaluate AI tools carefully, especially regarding transparency and rights compliance. Opt-out strategies, like those offered by Spawning, are becoming more relevant than ever.
Translating the growing industry consensus into actionable standards will be complex, with key questions remaining around technical implementation, sustainable business models, and balancing innovation with rights protection. As these frameworks evolve throughout 2025, we’ll be watching how different stakeholders navigate these challenges, and which new standards ultimately take hold.
Revisit Water & Music’s previous research on music AI: