暨南学报(哲学社会科学版)

作者：刘水美

单位：中国政法大学法律硕士学院。

关键词：人工智能,著作权侵权,数据训练,合理使用,转换性使用

数据训练是影响人工智能最终功能与效果实现的决定性因素,亦是引发众多著作权纠纷的主要原因。被告时常以著作权合理使用为侵权抗辩理由,学界业界对此莫衷一是。通过对人工智能情境下数据训练著作权侵权规制路径主要模式的归纳与辨析后得出,著作权合理使用为数据训练著作权侵权规制路径的最优解。目前,我国著作权合理使用法律规则与人工智能数据训练的使用情形尚不能适配。美国“转换性使用”标准作为扩大合理使用情形的一般判断规则,因存在理论与实践瑕疵,不适宜将其直接移植入我国法律中来。人工智能数据训练的合法化符合我国社会政策发展需要,有助于提升人工智能新质生产力并可促进人工智能产业国内外公平发展。以上可为我国设置具体的人工智能数据训练合理使用法律条款释疑及证成。"/>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"},"article":{"keywordList_cn":["人工智能","著作权侵权","数据训练","合理使用","转换性使用"],"juan":"46","zhaiyao_cn":"数据训练是影响人工智能最终功能与效果实现的决定性因素,亦是引发众多著作权纠纷的主要原因。被告时常以著作权合理使用为侵权抗辩理由,学界业界对此莫衷一是。通过对人工智能情境下数据训练著作权侵权规制路径主要模式的归纳与辨析后得出,著作权合理使用为数据训练著作权侵权规制路径的最优解。目前,我国著作权合理使用法律规则与人工智能数据训练的使用情形尚不能适配。美国“转换性使用”标准作为扩大合理使用情形的一般判断规则,因存在理论与实践瑕疵,不适宜将其直接移植入我国法律中来。人工智能数据训练的合法化符合我国社会政策发展需要,有助于提升人工智能新质生产力并可促进人工智能产业国内外公平发展。以上可为我国设置具体的人工智能数据训练合理使用法律条款释疑及证成。","endNoteUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/article/getTxtFile.do?fileType=EndNote&id=7105","reference":"","bibtexUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=BibTeX&id=7105","articleType":"","abstractUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/10.11778/j.jnxb.20241795","qi":"11","id":7105,"nian":2024,"bianHao":"1733127342650-568009085","zuoZheEn_L":"LIU Shuimei","juanUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/Y2024","shouCiFaBuRiQi":"2024-12-02","qiShiYe":"60","qiUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/Y2024/V46/I11","lanMu_cn":"数字治理","pdfSize":"1459","zuoZhe_CN":"刘水美","risUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=Ris&id=7105","title_cn":"人工智能数据训练著作权合理使用法律规则路径探究","doi":"10.11778/j.jnxb.20241795","jieShuYe":"73","keywordList_en":["artificial intelligence","copyright infringement","data training","fair use","transformative use"],"endNoteUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/article/getTxtFile.do?fileType=EndNote&id=7105","zhaiyao_en":"Data training profoundly influences the functionality and effectiveness of artificial intelligence (AI). Its involvement in the use of copyrighted works has triggered numerous copyright disputes both domestically and internationally, with the fair use principle frequently invoked as a defense in such cases. The legal rules and application of fair use in China have long been subject to various judicial challenges, which are also reflected in determining fair use in the context of AI data training. Therefore, there is an urgent need to consider the legal rules of fair use for AI data training and shape a Chinese approach.There are divergent views on copyright infringement in AI data training and its regulatory pathways in academia and practice. Regulatory pathways for copyright infringement in AI data training can be categorized into four main models: acknowledging the existence of copyright infringement, acknowledging copyright infringement but claiming statutory licensing, acknowledging copyright infringement but claiming fair use, and denying the existence of copyright infringement. After analysis, it is concluded that fair use is the optimal solution for regulating copyright infringement in data training.However, China's current copyright fair use rules have not yet been adapted to address AI data training issues. The transformative use test in the U.S., as a general rule for expanding fair use scenarios, is not suitable for direct transplantation into Chinese law to determine the new types of uses, including AI data training, due to its theoretical and practical flaws. Based on that the legalization of AI data training aligns with China's social policy development needs, helps to enhance the new productive forces, and fosters the fair growth of the domestic and international AI industry, China should establish specific AI data training legal provisions for fair use. However, it should be noted that even with the establishment of specific legal provisions for fair use in AI data training, the substantial interests of copyright holders must not be ignored.This article conducts in-depth research and comparative analysis of the current mainstream regulatory pathways for copyright infringement in AI data training. After concluding that copyright infringement in AI data training should be regulated through fair use, it further clarifies and justifies the establishment of specific legal provisions for fair use in AI data training. Through comparative analysis with other relevant statutory proposals and by analyzing the objectives and implementation principles of specific legal provisions for fair use in AI data training, this article creatively puts forward detailed design suggestions for such provisions. In the context of AI data training, concise fair use legal provisions are proposed to comprehensively and effectively achieve the purposes and balancing spirit of copyright law, aiming to minimize frictions and maximize benefits for all the stakeholders.","bibtexUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/article/getTxtFile.do?fileType=BibTeX&id=7105","abstractUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/10.11778/j.jnxb.20241795","zuoZheCn_L":"刘水美","juanUrl_cn":"https://jnxb.jnu.edu.cn/skb/CN/Y2024","lanMu_en":"","qiUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/Y2024/V46/I11","zuoZhe_EN":"LIU Shuimei","risUrl_en":"https://jnxb.jnu.edu.cn/skb/EN/article/getTxtFile.do?fileType=Ris&id=7105","title_en":"Exploring Legal Pathways for Fair Use of Copyright for AI Data Training","hasPdf":"true"},"authorNotes_cn":["刘水美,中国政法大学法律硕士学院。"]};if(window.metaData && (!window.metaData.authorNotesCommon_cn && !window.metaData.authorNotesCorresp_cn && window.metaData.authorNotes_cn)){window.metaData.authorNotesCommon_cn = window.metaData.authorNotes_cn;}if(window.metaData && (!window.metaData.authorNotesCommon_en && !window.metaData.authorNotesCorresp_en && window.metaData.authorNotes_en)){window.metaData.authorNotesCommon_en = window.metaData.authorNotes_en;}var _nlmdtdXml = $("#article_nlmdtdXml").val();var magDir = _nlmdtdXml.replace(/\/[^\/]+$/,'');window.metaData.magDir = magDir;new mag_vue({ el: '#metaVue', //dataUrl: mag_currentQikanUrl() + '/EN/article/getRichHtmlJson.do?articleId='+$("#articleId").val(), data:window.metaData, loading:true, doTextFun:function(text){ return doDataJsonText(text); 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来源：2024年第11期

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