RIAA and IFPI Launch Voluntary AI-Labeling System for Music Tracks

On July 10 2026, the Recording Industry Association of America (RIAA) and the International Federation of the Phonographic Industry (IFPI) announced a joint program that will add voluntary labels to music tracks, indicating whether generative artificial intelligence (AI) was used in their creation. The labels will differentiate between tracks that are fully AI‑generated and those that are AI‑assisted, and will be applied across digital music services and other partners.

The initiative follows a sharp rise in AI‑generated content on streaming platforms. In April, Deezer reported that 44 % of all new music uploaded to its service is AI‑generated, amounting to roughly 75 000 synthetic tracks per day. Apple Music has confirmed that more than a third of its uploads are 100 % AI. Industry analysts refer to this trend as “AI slop” – low‑effort, high‑volume synthetic music produced for click‑bait or monetisation – prompting record‑industry bodies to seek a way to inform listeners.

According to the joint statement released by the RIAA and IFPI, “Fans want to know whether and how generative AI has been used in the music to which they listen.” The statement added that the labels would provide an immediately understandable and easily scalable approach to transparency. The program is voluntary; labels will be added by rights holders who choose to participate.

The launch is supported by a broad coalition of music‑industry organisations. In addition to the RIAA and IFPI, the announcement lists the American Association of Independent Music (A2IM), the Worldwide Independent Network (WIN), the Independent Music Companies Association (IMPALA), the Recording Academy (The Grammys), the Screen Actors Guild‑American Federation of Television and Radio Artists (SAG‑AFTRA), and the Human Artistry Campaign. WIN’s CEO Noemí Planas said the labeling effort aligns with the WIN Principles for Generative AI, which emphasise transparency, safety, and a human‑centred approach.

Harvey Mason Jr., CEO of The Grammys, highlighted the importance of authorship and artistic intent. He noted that “giving artists the ability to tell that story strengthens trust and supports a more sustainable future for music.” The Grammys’ involvement signals the industry’s willingness to address the creative and legal questions that arise when AI is used in music production.

The labels will be applied at the track level, allowing streaming services to display a simple icon or tag next to the song title. The RIAA and IFPI have not yet specified the exact visual design, but the intent is to make the information accessible to all users. The program also includes a framework for collecting data on the prevalence of AI‑generated content, which could inform future policy and licensing discussions.

Industry observers see the initiative as a response to the growing volume of AI‑generated music and the challenges it poses for copyright enforcement and royalty distribution. By making the use of AI explicit, the program aims to protect artists’ rights and give listeners a clearer understanding of the creative process behind the tracks they enjoy.

The RIAA and IFPI have not yet released a timetable for when the labels will be rolled out across services. The partners are currently working with streaming platforms to develop technical specifications and testing procedures. The program’s success will depend on the willingness of rights holders to adopt the labeling system and on the cooperation of digital music services.

In summary, the RIAA‑IFPI AI‑labeling program is a voluntary, industry‑wide effort to mark music tracks that involve generative AI. It responds to the rapid increase in AI‑generated content on platforms such as Deezer and Apple Music, and it seeks to provide transparency for listeners while protecting artists’ creative integrity.

The initiative is still in its early stages, and further details about implementation and participation will likely emerge in the coming months as the industry tests the system on a limited scale.