Generative AI has music creators and publishers rightly concerned about losing control, losing credit, and losing compensation for their work because AI companies don’t disclose how they’re using music when generating new outputs. Those concerns are valid and shared by SOCAN and many others in Canada’s cultural sector.
We use the term output because music is created by people, not by AI platforms. In this context, an output refers to the material produced by an AI system, distinct from the human-made music it may draw upon.
One emerging solution is called attribution technology, or more simply, tools that can identify when a music creator’s work has been used to create a new AI-generated output.
As part of our ongoing commitment to protecting and advancing our members’ rights, SOCAN has entered an exploratory relationship with Musical AI to understand how attribution technology can address our concerns on the lack of transparency. Musical AI is a Canadian company with deep experience in big data, AI, rights management, and the music industry, including lived experience as musicians. They believe in your talent, skill, and craft as much as we do.
This relationship is not permission for AI to use your music or a shift away from human creativity.
This relationship is a proactive step to protect the rights of music creators and publishers in a future that includes AI.
Our goal is to better understand whether this relationship can provide meaningful benefits to members.
Read more about what we’ll work on together.
Building real transparency
We’re testing attribution tools that could show creators when their music is used by AI systems or to influence fully AI- generated outputs.
Today, music creators have no visibility at all.
Ensuring rights can be enforced
Without attribution, music creators and publishers cannot reliably prove when their work was used in AI-generated outputs and cannot be paid. This project explores whether attribution technology is accurate and reliable enough to facilitate compensation for the use of your works by AI systems.
Securing future revenue streams
Solutions for valuing music fairly are required when presented with new technology. We’re exploring how creators could be compensated when their work contributes to AI systems or appears in AI outputs.
Will SOCAN member data be shared or used when testing the tool?
For the initial pilot, SOCAN and Musical AI will test a mutually agreed selection of publicly available AI-generated outputs using Musical AI’s existing attribution technology. Any non-public information supplied for the pilot will remain private, be used only for the pilot, and be securely deleted when testing is complete. Any testing involving data from SOCAN members will proceed only after SOCAN obtains their explicit permission.