{"title":"生成式人工智能训练阶段的版权保护:以产业为导向的美国法律、以权利为导向的欧盟法律,以及联合国人工智能国际治理制度下生成式人工智能训练的公平报酬权","authors":"Kaigeng Li, Hong Wu, Yupeng Dong","doi":"10.1016/j.clsr.2024.106056","DOIUrl":null,"url":null,"abstract":"<div><div>Generative AI relies on simulating and learning from complex data distributions to automatically generate new, meaningful content from large datasets. Training generative AI models carries the risk of copyright infringement. How can we balance the development of generative AI technology with copyright protection during the training stage of these models? In contrast to existing legal scholarship, this article conducts a comparative study of industry-oriented U.S. copyright law and rights-oriented EU copyright law. It draws insights from the concepts of the tragedy of the commons and the tragedy of the anti-commons, proposing the creation of fair remuneration rights for Generative AI training under the UN's international governance regime for AI. This article offers a typological analysis of potential operational models for fair remuneration rights in Generative AI training, drawing analogies to existing remuneration rights in international copyright treaties. The goal is to provide an open framework for further discussion within the international academic community.</div></div>","PeriodicalId":51516,"journal":{"name":"Computer Law & Security Review","volume":"55 ","pages":"Article 106056"},"PeriodicalIF":3.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Copyright protection during the training stage of generative AI: Industry-oriented U.S. law, rights-oriented EU law, and fair remuneration rights for generative AI training under the UN's international governance regime for AI\",\"authors\":\"Kaigeng Li, Hong Wu, Yupeng Dong\",\"doi\":\"10.1016/j.clsr.2024.106056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Generative AI relies on simulating and learning from complex data distributions to automatically generate new, meaningful content from large datasets. Training generative AI models carries the risk of copyright infringement. How can we balance the development of generative AI technology with copyright protection during the training stage of these models? In contrast to existing legal scholarship, this article conducts a comparative study of industry-oriented U.S. copyright law and rights-oriented EU copyright law. It draws insights from the concepts of the tragedy of the commons and the tragedy of the anti-commons, proposing the creation of fair remuneration rights for Generative AI training under the UN's international governance regime for AI. This article offers a typological analysis of potential operational models for fair remuneration rights in Generative AI training, drawing analogies to existing remuneration rights in international copyright treaties. The goal is to provide an open framework for further discussion within the international academic community.</div></div>\",\"PeriodicalId\":51516,\"journal\":{\"name\":\"Computer Law & Security Review\",\"volume\":\"55 \",\"pages\":\"Article 106056\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Law & Security Review\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0267364924001225\",\"RegionNum\":3,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"LAW\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Law & Security Review","FirstCategoryId":"90","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0267364924001225","RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LAW","Score":null,"Total":0}
Copyright protection during the training stage of generative AI: Industry-oriented U.S. law, rights-oriented EU law, and fair remuneration rights for generative AI training under the UN's international governance regime for AI
Generative AI relies on simulating and learning from complex data distributions to automatically generate new, meaningful content from large datasets. Training generative AI models carries the risk of copyright infringement. How can we balance the development of generative AI technology with copyright protection during the training stage of these models? In contrast to existing legal scholarship, this article conducts a comparative study of industry-oriented U.S. copyright law and rights-oriented EU copyright law. It draws insights from the concepts of the tragedy of the commons and the tragedy of the anti-commons, proposing the creation of fair remuneration rights for Generative AI training under the UN's international governance regime for AI. This article offers a typological analysis of potential operational models for fair remuneration rights in Generative AI training, drawing analogies to existing remuneration rights in international copyright treaties. The goal is to provide an open framework for further discussion within the international academic community.
期刊介绍:
CLSR publishes refereed academic and practitioner papers on topics such as Web 2.0, IT security, Identity management, ID cards, RFID, interference with privacy, Internet law, telecoms regulation, online broadcasting, intellectual property, software law, e-commerce, outsourcing, data protection, EU policy, freedom of information, computer security and many other topics. In addition it provides a regular update on European Union developments, national news from more than 20 jurisdictions in both Europe and the Pacific Rim. It is looking for papers within the subject area that display good quality legal analysis and new lines of legal thought or policy development that go beyond mere description of the subject area, however accurate that may be.