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This paper critically examines the limitations of federated learning in creative AI, arguing that current models allow contributors to consent to training but lack agency over the resulting model's governance and federation. The authors propose a framework for "creative data commons" where stewardship extends beyond storage and circulation to actively govern the learned models themselves through design principles like legible terms and first-class refusal states. This work shifts the focus from mere privacy preservation to structural equity in how AI systems are built and controlled by their training communities.
Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI
arXiv:2609.03800v1 Announce Type: new Abstract: Federated learning is increasingly presented as a privacy-preserving advance: personal data remain on the device, and only model updates are shared. It borrows the vocabulary of the federated social web, yet inverts its logic, distributing computation while the resulting model stays with whoever convened the training. We argue that federation is not in itself a remedy for extractive AI, because outcomes depend on who governs the data and the model and who has agency over the practices that shape them. We describe three layers at which a creative community can hold its work: storage, circulation, and learning. Examining artist-governed trusts, cooperatives, and consent infrastructures, we show that creator governance is established at storage and circulation but stops at learning: contributors can consent to training, yet have little say over the resulting model or its federation. We map the research space this opens, pairing technical open problems with the human questions from which they unfold. We propose four design principles for a creative data commons that governs models and their federation, not only datasets: govern the model, not only the corpus; make the terms legible at the moment of contribution; design for refusal as a first-class state; and decide stewardship in the open and account for it.
Authors:Phoenix Perry, George Simms, Elizabeth Wilson, Yasmine Boudiaf, Nick Bryan-Kinns, Tega Brain, R. Luke DuBois, Alix Rule, Rachel Meade Smith, Kelani Nichole, Atharva Pravin Pawar, Rebecca Fiebrink
Abstract:Federated learning is increasingly presented as a privacy-preserving advance: personal data remain on the device, and only model updates are shared. It borrows the vocabulary of the federated social web, yet inverts its logic, distributing computation while the resulting model stays with whoever convened the training. We argue that federation is not in itself a remedy for extractive AI, because outcomes depend on who governs the data and the model and who has agency over the practices that shape them. We describe three layers at which a creative community can hold its work: storage, circulation, and learning. Examining artist-governed trusts, cooperatives, and consent infrastructures, we show that creator governance is established at storage and circulation but stops at learning: contributors can consent to training, yet have little say over the resulting model or its federation. We map the research space this opens, pairing technical open problems with the human questions from which they unfold. We propose four design principles for a creative data commons that governs models and their federation, not only datasets: govern the model, not only the corpus; make the terms legible at the moment of contribution; design for refusal as a first-class state; and decide stewardship in the open and account for it.
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2609.03800 [cs.AI]
(or arXiv:2609.03800v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.03800 arXiv-issued DOI via DataCite (pending registration)
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