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

2026-09-03

AI Summary

论文探讨了联邦学习中的模型治理问题，提出创意社区应在存储、流通和学习层面进行治理，并提出了四个设计原则以实现对模型及其联合的治理。

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.

Problem

• federated learning
• data governance
• model governance
• creative AI

Innovation

• federated learning
• creator governance
• data and model governance
• creative AI