AI Transparency for Music
Is this AI music?
It’s the wrong question.
Not because AI doesn’t matter — but because modern music creation is rarely binary. Most records are hybrid: prompting, editing, resampling, mixing, performance, and taste.
“AI / NOT AI” collapses nuance
Detection tools, watermarks, and simple labels can flatten fundamentally different creative processes into the same bucket. Two tracks can both be “AI,” but one is 5% assistive cleanup and the other is 95% synthetic performance.
Hybrid creation is the default
A track might start with a prompt, then be rewritten by a human, replayed with instruments, edited, resampled, mixed, and mastered — using a blend of traditional and AI-assisted tools.
The pipeline is multi-tool, multi-stage, and editable
Bottom line: when content can be transformed repeatedly (stem edits, re-recordings, resampling, human overdubs), the “is this AI?” question becomes less useful than documenting how it was made.
What actually matters
Instead of asking “is this AI?”, ask questions that preserve creative intent and accountability:
Disclosure beats guessing
TRAICE is a creator-facing platform for voluntary, structured AI disclosure — like liner notes or Genius annotations, but focused on process: roles, tools, and context.