管控模型，而非仅管控数据：创意人工智能中的存储、流通与学习
Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI
- University of the Arts London（伦敦艺术大学）
- New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究指出联邦学习未解决创意AI的管控问题，提出创意数据公地的四项设计原则，以实现对模型及联邦的管控而非仅对数据的管控。
AI中文摘要：
联邦学习作为一种隐私保护的进展日益受到关注：个人数据保留在设备上，仅共享模型更新。它借用了联邦社交网络的术语，却颠倒了其逻辑，即分布式计算，而生成的模型则归召集训练的一方所有。我们认为，联邦本身并非提取式人工智能的解决方案，因为结果取决于谁管控数据和模型，以及谁对塑造它们的实践拥有自主权。我们描述了创意社群可掌控其工作的三个层面：存储、流通与学习。通过研究艺术家管控的信托机构、合作社和同意基础设施，我们发现创作者管控在存储和流通层面得以建立，但在学习层面止步：贡献者可以同意训练，却对生成的模型及其联邦几乎没有话语权。我们梳理了由此开辟的研究空间，将技术开放问题与催生这些问题的人文问题配对。我们为创意数据公地提出四项设计原则，该公地管控模型及其联邦，而非仅管控数据集：管控模型，而非仅管控语料库；在贡献时使条款清晰易懂；将拒绝设计为一等状态；公开决定管理职责并对其负责。
英文摘要：
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.
