Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI

The paper argues that federated learning, while touted as privacy‑preserving, still concentrates control over the resulting model with the entity that orchestrates training. It identifies three layers—storage, circulation, and learning—where creative communities can exert governance, noting that current practices allow consent for training but not for model ownership or federation. The authors propose four design principles for a creative data commons that extends governance to models, ensures legibility of terms at contribution, incorporates refusal as a first‑class state, and makes stewardship transparent and accountable.

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