On the Protection via Neighboring Right of Artificial Intelligence Generated Content (AIGC)

The understanding and application of copyright law with regards to AI generated images has created significant challenges. China and the United States demonstrate that the judicial bodies of the two countries have distinct differences in their attitude towards copyright protection of AI generated material. The reason is that the generation process of artificial intelligence generator includes two stages of user input instruction and automatic generation of artificial intelligence program. The discussion focuses on the personalized characteristics of the instructions in the user input stage and the automatic characteristics stage, which will determine whether the artificial intelligence generated material can be protected by copyright. This paper argues that the neighboring rights approach can better integrate these two stages. By comparing the object of rights, the subject of rights, and the content of rights between AI generated works and existing neighboring rights, it can be found that AI generated works are in conformity with neighboring rights, which can resolve the dilemma of "either this or that" choice faced by the copyright approach. However, neighboring rights are typical statutory rights and need to be coordinated with the copyright protection of the existing works used by artificial intelligence. Whether it is necessary to legislate on neighboring rights for artificial intelligence generated works still awaits further observation over time.

Authors

Institutions

Publication Details

Journal
Humanities and Arts
Published
2026-02-26
DOI
https://doi.org/10.57237/j.ha.2026.01.001
Primary Topic
Law, AI, and Intellectual Property
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the Protection via Neighboring Right of Artificial Intelligence Generated Content (AIGC)

Rong Han
Humanities and Arts
Law, AI, and Intellectual Property
article

On the Protection via Neighboring Right of Artificial Intelligence Generated Content (AIGC)

Rong Han
article en

Abstract

The understanding and application of copyright law with regards to AI generated images has created significant challenges. China and the United States demonstrate that the judicial bodies of the two countries have distinct differences in their attitude towards copyright protection of AI generated material. The reason is that the generation process of artificial intelligence generator includes two stages of user input instruction and automatic generation of artificial intelligence program. The discussion focuses on the personalized characteristics of the instructions in the user input stage and the automatic characteristics stage, which will determine whether the artificial intelligence generated material can be protected by copyright. This paper argues that the neighboring rights approach can better integrate these two stages. By comparing the object of rights, the subject of rights, and the content of rights between AI generated works and existing neighboring rights, it can be found that AI generated works are in conformity with neighboring rights, which can resolve the dilemma of "either this or that" choice faced by the copyright approach. However, neighboring rights are typical statutory rights and need to be coordinated with the copyright protection of the existing works used by artificial intelligence. Whether it is necessary to legislate on neighboring rights for artificial intelligence generated works still awaits further observation over time.

Humanities and ArtsVol. 5(1)
Zhaoqing University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 45%
Law, AI, and Intellectual Property
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.