生成式 AI 时代的版权治理：arXiv 研究提出六项工具治理设计

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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:2610.17002v1 [cs.CY] for this version)
	https://doi.org/10.48550/arXiv.2610.17002 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dmitry Kochetkov [view email]
[v1] Mon, 12 Oct 2026 15:50:05 UTC (659 KB)