The Comparative Analysis of Fair Use of Works in Machine Learning
Before generative AI outputs the content, it copies a large amount of text content. This process is machine learning. For the development of artificial intelligence technology and cultural prosperity, many countries have included machine learning within the scope of fair use. However, China’s copyri...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2023/27/shsconf_icprss2023_01015.pdf |
| _version_ | 1797634747107639296 |
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| author | Zhu Yunlan |
| author_facet | Zhu Yunlan |
| author_sort | Zhu Yunlan |
| collection | DOAJ |
| description | Before generative AI outputs the content, it copies a large amount of text content. This process is machine learning. For the development of artificial intelligence technology and cultural prosperity, many countries have included machine learning within the scope of fair use. However, China’s copyright law currently does not legislate the fair use of machine learning works. This paper will construct a Chinese model of fair use of machine learning works through comparative analysis of the legislation of other countries. This is a fair use model that balances the flexibility of the United States with the rigor of the European Union. |
| first_indexed | 2024-03-11T12:12:54Z |
| format | Article |
| id | doaj.art-94d394dccd884d93bc37d2e64ea57c61 |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-03-11T12:12:54Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-94d394dccd884d93bc37d2e64ea57c612023-11-07T10:41:40ZengEDP SciencesSHS Web of Conferences2261-24242023-01-011780101510.1051/shsconf/202317801015shsconf_icprss2023_01015The Comparative Analysis of Fair Use of Works in Machine LearningZhu Yunlan0Shenyang University of TechnologyBefore generative AI outputs the content, it copies a large amount of text content. This process is machine learning. For the development of artificial intelligence technology and cultural prosperity, many countries have included machine learning within the scope of fair use. However, China’s copyright law currently does not legislate the fair use of machine learning works. This paper will construct a Chinese model of fair use of machine learning works through comparative analysis of the legislation of other countries. This is a fair use model that balances the flexibility of the United States with the rigor of the European Union.https://www.shs-conferences.org/articles/shsconf/pdf/2023/27/shsconf_icprss2023_01015.pdf |
| spellingShingle | Zhu Yunlan The Comparative Analysis of Fair Use of Works in Machine Learning SHS Web of Conferences |
| title | The Comparative Analysis of Fair Use of Works in Machine Learning |
| title_full | The Comparative Analysis of Fair Use of Works in Machine Learning |
| title_fullStr | The Comparative Analysis of Fair Use of Works in Machine Learning |
| title_full_unstemmed | The Comparative Analysis of Fair Use of Works in Machine Learning |
| title_short | The Comparative Analysis of Fair Use of Works in Machine Learning |
| title_sort | comparative analysis of fair use of works in machine learning |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2023/27/shsconf_icprss2023_01015.pdf |
| work_keys_str_mv | AT zhuyunlan thecomparativeanalysisoffairuseofworksinmachinelearning AT zhuyunlan comparativeanalysisoffairuseofworksinmachinelearning |