提出创意AI应治理模型而非仅数据，构建创作者主导的协作生态。
Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI
- 从存储、传播、学习三层面重构创作者对AI模型的控制权
- 现有联邦学习仍由发起方掌控模型，创作者无权干预训练结果
- 倡导开放治理、可拒绝参与、透明问责的设计原则
联邦学习常被视为保护隐私的技术进步：个人数据保留在设备端，仅共享模型更新。它借用去中心化社交网络的话语，却反转其逻辑——计算分散进行，但最终模型仍掌握在召集训练的一方手中。我们指出，联邦学习本身并非解决掠夺式AI的良方，因为结果取决于谁掌控数据与模型，以及谁拥有塑造实践的自主权。本文提出创意社区可从三个层面实现自我治理：存储、传播与学习。通过分析艺术家主导的信托、合作社与同意基础设施，发现创作者虽能对数据使用表示同意，但在模型生成及联邦过程中的话语权依然缺失。本文梳理这一研究空白，将技术难题与人文问题对应，提出四项设计原则：治理模型而非仅语料库；在贡献时使条款清晰可读；将拒绝视为首要状态；公开决定治理权并持续问责。
原文摘要 · Abstract (English)
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.
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