生成式 AI 时代的负责任版权治理研究
Governing Copyright Responsibly in the Era of Generative AI
AI 导读
一篇 arXiv 论文研究版权治理如何保护作品被用于大语言模型训练的作者。研究对 2019 至 2026 年间发布的 40 份立法、司法和软法文件（以欧盟和美国为主，英国、中国和国际机构为对照）进行编码，共得 1,337 个类别赋值。结果显示两个主要法域均以程序性而非权利性方式框架化保护，语料中没有任何文件建立面向作者的通用 AI 训练署名义务，论文据此提出六工具治理设计。
正文 · 原文
Abstract:Large language models are trained on collections that may include scholarly works, while authors generally lack work-level information about whether and how their works were used. This paper asks how copyright governance frames and operationalises the protection of such authors, and what a responsible alternative would require. Forty legislative, judicial and soft-law instruments issued between 2019 and 2026 were coded deductively against a framework derived from Responsible Research and Innovation theory, yielding 1,337 category assignments across 835 segments. The European Union and the United States are the primary cases; the United Kingdom, China and international bodies serve as comparators. Normative orientation and the distribution of responsibility dominate the corpus, while reflexivity is the least represented category and reaches its minimum in the US sub-corpus. Both primary jurisdictions frame protection procedurally rather than as an entitlement, and no instrument in the corpus creates a general author-facing attribution duty for the use of works in AI training. From the coded gaps, the paper derives a six-instrument governance design and a capacity-based allocation of responsibility among developers, publishers, collecting societies, funders, institutions, regulators, and individual authors.
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL) |
| Cite as: | arXiv:2610.17002 [cs.CY] |
| (or arXiv:2610.17002v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17002 arXiv-issued DOI via DataCite |
Submission history
From: Dmitry Kochetkov [view email]
[v1]
Mon, 12 Oct 2026 15:50:05 UTC (659 KB)
[v2]
Wed, 11 Nov 2026 07:44:18 UTC (660 KB)
来源：arXiv cs.AI · arxiv.org