Practical disclosure guide
How to disclose AI use in music
Disclosure isn’t about proving anything. It’s about preserving context — the kind that disappears once a track leaves your hard drive.
The 4 things to include
Name what you used (Suno, Udio, vocal models, stem separators, AI enhancers). If you used multiple tools, list them.
What did AI actually do? (melody draft, vocal generation, sound design, cleanup, mix assist, mastering, etc.)
Roughly how much of the final record came from AI output vs human decisions/edits? You’re describing a workflow, not submitting “proof.”
The important part: what you changed, chose, rejected, rewrote, performed, arranged, mixed, or finalized.
Example
Notice this isn’t “AI / NOT AI.” It’s a chain of steps — the same way liner notes describe who did what.
Why this matters
Binary labels erase nuance. Disclosure preserves it — for fans, collaborators, platforms, and future listeners trying to understand how a sound was actually made.
Publish the context, not a verdict
If your process used multiple tools across multiple stages, that’s normal. TRAICE is built for real workflows — not simplistic policing.