Tiered copyrightability for generative artificial intelligence: An empirical analysis of China and the United States judicial practices

Abstract The rapid advancement of generative artificial intelligence (AI) poses significant challenges to traditional copyright frameworks, intensifying debates over the copyrightability of AI‐generated outputs. By comparing judicial practices in China and the United States, it has been observed that the United States maintains a conservative stance of adhering to substantive control, while China demonstrates an inclusive approach through the criterion of creative contribution. Building upon this, this article transcends the traditional binary judgment model and constructs a tiered copyright determination model. Based on the level of human control and contribution in the AI generation process, it introduces dimensions such as technological controllability and density of human intent, classifying generative AI into three tiers: strong protection, weak protection, and non‐protection. Regarding the copyrightability of content generated by generative AI, this article argues that the issue should be addressed within the framework of copyright law itself. When human participation is involved and the substantial contribution of the direct user is reflected in the AI‐generated content, meeting the requirements for copyrightable works under copyright law, corresponding protective measures should be granted.

Authors

Institutions

Publication Details

Journal
AI Magazine
Published
2025-08-22
DOI
https://doi.org/10.1002/aaai.70018
Citations
1
Primary Topic
Law, AI, and Intellectual Property
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tiered copyrightability for generative artificial intelligence: An empirical analysis of China and the United States judicial practices

Zichun Xu, Zhilang Xu
1 citations
AI Magazine
Law, AI, and Intellectual Property
2.48
article

Tiered copyrightability for generative artificial intelligence: An empirical analysis of China and the United States judicial practices

Zichun Xu, Zhilang Xu
article en
1 citations

Abstract

Abstract The rapid advancement of generative artificial intelligence (AI) poses significant challenges to traditional copyright frameworks, intensifying debates over the copyrightability of AI‐generated outputs. By comparing judicial practices in China and the United States, it has been observed that the United States maintains a conservative stance of adhering to substantive control, while China demonstrates an inclusive approach through the criterion of creative contribution. Building upon this, this article transcends the traditional binary judgment model and constructs a tiered copyright determination model. Based on the level of human control and contribution in the AI generation process, it introduces dimensions such as technological controllability and density of human intent, classifying generative AI into three tiers: strong protection, weak protection, and non‐protection. Regarding the copyrightability of content generated by generative AI, this article argues that the issue should be addressed within the framework of copyright law itself. When human participation is involved and the substantial contribution of the direct user is reflected in the AI‐generated content, meeting the requirements for copyrightable works under copyright law, corresponding protective measures should be granted.

AI MagazineVol. 46(3)
Southwest University of Political Science & Law (CN), Sichuan University of Science and Engineering (CN)
Openalex Percentile: Top 9%
Law, AI, and Intellectual Property
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.