Apple Music Will Flag Songs Made With AI
The streaming service will show listeners when AI materially shaped a track as machine-made uploads surge.
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Apple Music will begin placing a visible “Made With AI” label on songs substantially created using artificial intelligence, moving AI disclosure from behind-the-scenes metadata to something listeners can see.
The labels will arrive later this year, according to an email Apple sent to music-industry partners that was reported by The Hollywood Reporter. Record labels and distributors will be required to disclose when AI was used to create a material portion of a release, including tracks primarily generated by an AI platform.
Apple defines such content as material “primarily derived from a generative AI service.”
The change builds on AI Transparency Tags introduced by Apple Music in March. Those metadata fields cover four parts of a release: the recording itself, composition or lyrics, artwork and music videos. The new step will make relevant disclosures visible to Apple Music users.
The policy comes as AI-generated music is arriving on streaming services far faster than listeners are embracing it.
Apple Music Vice President Oliver Schusser said in April that more than a third of music being submitted to the service each month was entirely AI-generated. Yet those tracks accounted for less than 0.5% of listening.
That mismatch has created a new problem for streaming platforms: producing a song has become dramatically easier, while determining how it was made is increasingly difficult.
Apple has developed technology capable of identifying AI-generated material and the models behind some submissions, according to Schusser. But the disclosure system will still depend substantially on labels, distributors and other content providers accurately reporting AI use.
That leaves a practical question over enforcement. A label can tell listeners when AI played a substantial role in making a song, but the system will be most useful only if undisclosed AI-generated tracks can also be reliably detected.
