暨南学报(哲学社会科学版)

作者：赵旭

单位：清华大学法学院。

关键词：生成式人工智能,合理使用,机器学习,转换性使用

基金：北京市社会科学基金项目“创新型权益与标识型权益二分框架下的知识产权损害赔偿体系化研究”(20FXB008)。

生成式人工智能的主要用途是模仿并生成新的作品,但最终生成的作品却可能会替代现有作品的市场,因而在合理使用的认定中需要重新评估生成式人工智能的商业性用途和对现有作品的市场替代后果。对于不具有特定模仿用途的生成式人工智能,由于事前获得许可面临高昂的交易成本并且在使用目的上又具有高度的转换性,原则上可以构成合理使用;但对于针对特定模仿对象的人工智能,不宜认定构成合理使用。在监管政策上,对于生成式人工智能应当采取包容审慎的监管政策,数据来源合法的要求并不意味着必须要事先取得所有训练数据的著作权人的许可。"/>template{display:none;}.mag-rich-xref-fn { cursor: pointer;}//长视频$(document).ready(function(){if($("#showLongArticleVideo") && $("#showLongArticleVideo").length && $("#showLongArticleVideo").length>0){mag_ajax_update({ele_id:'showLongArticleVideo',url:mag_currentQikanUrl() + "/CN/article/showLongArticleVideo.do?id="+$("#articleId").val()});}});$(document).ready(function(){window.metaData = {"journal":{"issn":"1000-5072","qiKanMingCheng_CN":"暨南学报（哲学社会科学版）","id":2,"qiKanMingCheng_EN":"Jinan Journal"},"fundList_cn":["北京市社会科学基金项目“创新型权益与标识型权益二分框架下的知识产权损害赔偿体系化研究”(20FXB008)。"],"article":{"keywordList_cn":["生成式人工智能","合理使用","机器学习","转换性使用"],"juan":"46","zhaiyao_cn":"生成式人工智能的主要用途是模仿并生成新的作品,但最终生成的作品却可能会替代现有作品的市场,因而在合理使用的认定中需要重新评估生成式人工智能的商业性用途和对现有作品的市场替代后果。对于不具有特定模仿用途的生成式人工智能,由于事前获得许可面临高昂的交易成本并且在使用目的上又具有高度的转换性,原则上可以构成合理使用;但对于针对特定模仿对象的人工智能,不宜认定构成合理使用。在监管政策上,对于生成式人工智能应当采取包容审慎的监管政策,数据来源合法的要求并不意味着必须要事先取得所有训练数据的著作权人的许可。","endNoteUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/article/getTxtFile.do?fileType=EndNote&id=6925","reference":"","bibtexUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=BibTeX&id=6925","articleType":"","abstractUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/10.11778/j.jnxb.20231762","qi":"3","id":6925,"nian":2024,"bianHao":"1712029777031-1127719519","zuoZheEn_L":"ZHAO Xu","juanUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/Y2024","shouCiFaBuRiQi":"2024-04-02","qiShiYe":"79","qiUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/Y2024/V46/I3","lanMu_cn":"数字法治","pdfSize":"1539","zuoZhe_CN":"赵旭","risUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=Ris&id=6925","title_cn":"生成式人工智能在机器学习中的合理使用问题","doi":"10.11778/j.jnxb.20231762","jieShuYe":"95","keywordList_en":["generative AI","fair use of copyrights","machine learning","transformative use"],"endNoteUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=EndNote&id=6925","zhaiyao_en":"Compared to previous analytical artificial intelligence (AI), generative AI technology possesses a more potent capability to imitate and generate works. It can learn from existing works while producing similar ones. Consequently, certain AI-generated works may present challenges to the established copyright market for human authors. Given that generative AI necessitates machine learning before generating works, it inevitably relies on existing copyrighted materials. However, whether such utilization can be considered copyright fair use requires further research.This article studies whether the machine learning process of generative AI can be categorized as fair use. In contrast to existing literature, this article examines the fair use issue through the lens of the varied purposes of generative AI. For generative AI lacking a specific intent to imitate, owing to the substantial transaction costs associated with obtaining ex-ante permission and the transformative nature of its purpose of use, it can generally qualify as fair use. However, generative AI designed to target specific objects of imitation should not be considered fair use.In analyzing generative AI without explicit objects of imitation, this article primarily considers the transaction costs required in obtaining permission and the transformative nature of its purpose of use. In most cases, AI encounters considerable transaction costs when utilizing works for machine learning purposes. Therefore, it becomes necessary to consider exempting AI developers from copyright infringement under the fair use doctrine. From a transformative perspective, utilizing others' works for machine learning purposes is inherently transformative and should thus be deemed fair use. However, when generative AI is specifically intended to imitate the works of a particular author, applying the fair use exception is inappropriate, for such imitation may significantly substitute the market demand for the original author's works. In such instances, developers of AI should seek permission from copyright holders.The contribution of this article's study of the fair use doctrine within AI's machine learning lies in its advocacy for a balanced regulatory approach toward AI. By emphasizing the need to strike a balance between technological advancement and copyright protection, it underscores the value of inclusive and moderate regulatory policies in fostering the innovative development of new technologies. Consequently, within the machine learning process, AI developers are generally not required to seek ex-ante permission from copyright owners for all training data. Moderate regulatory policies serve to prevent overly stringent copyright enforcement from impeding the advancement of new technologies. 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来源：2024年第3期

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