公平使用对每个人都有好处
作者、记者、创作者、学者、无障碍倡导者、法律专家、图书管理员、经济学家和商界领袖都表示支持公平使用这一长期原则，并将其应用于大赦国际的培训。
几十年来，公平利用在推动惠及所有人的创新方面发挥了至关重要的作用。 它帮助提供了重要的医学和科学突破和新技术，为包括作者和记者在内的无数人带来了真正和积极的差异。 与此同时，《纽约时报》的官司要求每个人都无视半个世纪的法律历史和进步，在自己狭隘地追求一个不应有的报酬日时抛弃公平使用的长期原则。
各领域的专家和领导人都认识到，公平使用是一种双赢做法，惠及所有人，他们与法院分享了观点，为公平使用提出辩护状。
以他们自己的话说，公共法庭备案目录上有完整的档案，您可在此阅读更多关于公平使用和AI的资料。
- AI进展
- 美国盲人理事会、研究图书馆协会、美国图书馆协会、消费者技术协会和商业软件联盟
- 作者联盟
- 引擎和开发者联盟
- 美国创新基金会
- 约书亚·甘斯
- 凯文·凯利
- 马修・萨格
- 迈克尔·沃尔和莎拉·罗斯·西斯金德
- 公共知识
- 罗恩·苏斯金德和布莱恩·L·弗雷
- 美国政府
AI 进展
法院应拒绝原告试图将版权扩展到其既定界限之外。 AI培训将版权作品用于根本不同的目的，不向公众提供这些作品的替代访问，也不会造成《版权法》可识别的市场损害。 相反，它能够提供各种已经带来公众利益的变革性技术。 版权的存在是为了促进创造力和进步，而不是压制合法创新或保护作者免受竞争。 因此，解决的公平使用原则迫使人们得出结论，基因AI培训是公平的使用。
美国盲人理事会、研究图书馆协会、美国图书馆协会、消费者技术协会和商业软件联盟
本案涉及的公司多于开发大型语言模型(LLMs)的公司。 它影响到医学研究人员和病人、为不同学习需求的学生服务的教育工作者、寻求更大独立性的残疾人、应用农业知识的农民、开发新表达形式的独立创造者以及从软件到法律的工业部门。 这些社区都关心学习和学习广泛和多样的知识来源的能力，以及获得使这种学习有用的工具。 许多人依赖于其他人开发的系统，包括开源AI系统，而不是培训模型本身。 公平使用支持他们学习、发现、交流和创造的能力。 对公平使用培训的限制将影响到建立大赦国际的机构以及使用这种培训的人。
作者联盟
市场稀释理论要求版权法保护权利人免受它以前从未限制过的经济现象的影响。 新作品可以减少旧作品的销售量，能见度或价值，而不复制其受保护的表达。 《版权法》故意将竞争置于版权所有人控制之外。 因此，阿米库斯敬请法院认定，“市场稀释”即来自不侵犯产出，包括那些拥有相同主题或流派、具有未受保护特征或为相同受众竞争的产出的总体竞争压力，不是《美国法典》第17篇第107(4)节规定的可察觉的市场损害。 拒绝这一理论将保留第二巡回法院一贯坚持的区别：版权保护作者不受被保护的表达的替代，而不是妨碍仅仅与之竞争的新表达的成功。
引擎和开发者联盟
Startups使用基础模型证明，使用版权作品训练AI模型是一种公平的用途，而不是侵犯版权。 培训一个基础模型的目的不是要复制它所学习的作品；而是要建立一个通用工具，其他人可以适应新的目的。 最终开办企业的选择与任何培训数据集中的工作目的没有相似之处。
美国创新基金会
在人工智能培训能够创造新的知识、表达、软件和其他能力的情况下，这些好处影响到要素四对使用市场影响的评估。 这种推理支持在这里的新应用：考虑到能够进行新分析、软件和其他生产性工作的能力的国家安全价值。
约书亚·甘斯
战略管理教授
版权不仅支持创造内容的动力，而且支持建立从内容中学习的工具的动力，而一项将每一件可转移销售视为可诉销售的规则可能会削弱第二套，而不可靠地保护第一套。
凯文·凯利
作者
AI是一种变革性技术，加速了艺术和科学领域无数学科小说作品的创作。 Kelly先生在AI如何推进文学领域方面有经验。 他认为，每个AI模型都可以被理解为一种全新的媒介，为作者提供创建新文本和想法的能力，以及理性的能力。 因此，对原告公平使用的裁决将使原告获得远远超出其原先意图给予的有限垄断。 这将阻碍能够传播以前难以想象的大量信息的技术的发展。 这也将阻碍艺术和科学领域新的思想模式和新的原创作品的创作。 阿米库斯将这份简报存档，以支持被告人的论点，即使用版权作品训练大型语言模型是17 U.S.C.§107下的一种公平使用。 基于他一生对写作的追求，对版权所有者提供的保护对凯利先生很重要。 同样重要的是为可允许使用的版权材料和变革性技术的发展保留空间。 否则，版权将扼杀极cre 其存在是为了鼓励有意识、创新和知识传播。 公平使用原则对于确保这种平衡至关重要。
马修·萨格
法学教授
在本案中，被告利用原告的作品进行人工智能培训具有很大的变革性，并强烈主张找到公平用途，因为在现实条件下，被告模型的产出与培训过程中制作的隐形中间拷贝没有任何相似之处。
迈克尔·沃尔和莎拉·罗斯·西斯金德
音乐家和作曲家/喜剧演员和作家
这份简报提供了一个窗口，通过依靠版权保护和AI工具维持生计的两位独立创作者的工作，了解这句话在实践中的含义。 阿米西·莎拉·罗斯·西斯金德(英语：Sarah Rose Siskind)和迈克尔·沃尔(英语：Michael Wall)是创作和许可原创作品的艺术家。 但是，他们利用AI来处理围绕创造过程的工作，并在资源较丰富的竞争者充斥的市场上发展自己的企业。 对阿米西来说，大赦国际并不取代创造力或加快其创造过程。 大赦国际帮助他们创造更多，并将其工作扩展到新的表达方式。 阿米西的经验强调了美国的观点，即AI工具“可以而且应该有助于在主流出版商和独立出版商之间公平竞争。 ” Siskind在她业务的每个方面都使用AI，从日常的行政任务到开发想法和引进新的客户。 与AI一起，她的非创作性作品更快，成本更低，创作性作品也更好。 她表示：「我认为现在没有AI，同样，AI帮助Wall管理他的音乐业务的业务需求，降低成本，并投入更多的时间来编曲。 音乐和西斯金德一样，他怀疑自己的业务能否在没有AI的情况下以目前的形式生存下去。
公共知识
公平使用有助于人们浏览数码世界给我们带来的大量信息的受保护工具，包括图像搜索和全文书籍搜索。 见Perfect 10, Inc. v. Amazon.com, Inc., 508 F.3d 1146, 1164–68 (9th Cir. 2007); Authors Guild v. Google, Inc., 804 F.3d 202, 216–17, 225 (2d Cir. 2015) (“Google Books”). 它还保护图书馆向有印刷残疾的读者提供无障碍数字书籍。 见Authors Guild, Inc. v. Hathi Trust, 755 F.3d 87, 101–03 (2d Cir. 2014). 并保护免费在网上公布纳入法律的技术标准，承认“免费和方便地利用法律的重大公共好处”。 Am. Soc 'y for Testing & Matericies v. Public.Resources.Org, Inc., 82 F.4th 1262, 1271–72 (D.C. Cir. 2023). 公平使用同样有助于确保大赦国际的利益惠及广大公众，而不是由少数特权阶层控制。
罗恩·苏斯金德和布莱恩·Frye
Pulitzer获奖记者/法学教授
OpenAI利用报纸文章来训练其遗传性的AI模型，他们委托并发表报纸文章，希望出售消费者查阅这些文章的机会。 它们保留并继续行使销售消费者获得其物品的专有权利。 OpenAI使用这些物品来训练它的AI模型
美国政府
在这次诉讼中，"纽约时报"试图缩小公平使用学说的范围，以排除OpenAI的大型语言模型(LLMs)的培训。 其结果将不符合版权法的基本原则，并严重阻碍“科学和实用艺术的进步”。 如下文所述，除了《纽约时报》本身之外，包括独立媒体和开办媒体在内的出版物正在利用LLMs进行新闻和其他活动。 并且除了这个诉讼的主题之外，LLMs已经在帮助跨领域的研究人员实现重大突破。 在对公平使用理论的误解下限制LLM的发展将阻碍这种创造性和科学的进步，同时阻碍美国的繁荣和经济流动性。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Fair use benefits everyone
Authors, journalists, creators, academics, accessibility advocates, legal experts, librarians, economists and business leaders are voicing their support for the longstanding principle of fair use and its application to AI training.
For decades, fair use has played a critical role in driving innovation that benefits everyone. It has helped deliver important medical and scientific breakthroughs and new technologies that have made real and positive differences for countless people, including authors and journalists. The New York Times’ lawsuit, meanwhile, asks everyone to ignore a half century of legal history and progress, and toss aside the longstanding principle of fair use in their own narrow pursuit of an undeserved payday.
Experts and leaders across a wide range of fields have recognized that fair use is a win-win that benefits everyone, and have shared their perspectives with the Court in filing briefs in support of fair use.
Below you’ll find excerpts from some of those submissions, in their own words. The full filings are available on the public court docket, and you can read more about fair use and AI here.
- AI Progress
- American Council of the Blind, Association of Research Libraries, American Library Association, Consumer Technology Association & Business Software Alliance
- Authors Alliance
- Engine and Developers Alliance
- Foundation for American Innovation
- Joshua S. Gans
- Kevin Kelly
- Matthew Sag
- Michael Wall and Sarah Rose Siskind
- Public Knowledge
- Ron Suskind and Brian L. Frye
- United States Government
AI Progress
The Court should reject Plaintiffs’ attempt to extend copyright beyond its established limits. AI training uses copyrighted works for a fundamentally different purpose, does not provide the public with substitute access to those works, and does not cause the market harm cognizable under the Copyright Act. Instead, it enables a broad array of transformative technologies that are already delivering public benefits. Copyright exists to promote creativity and progress, not to suppress lawful innovation or protect authors from competition. Settled fair use principles therefore compel the conclusion that generative AI training is fair use.
American Council of the Blind, Association of Research Libraries, American Library Association, Consumer Technology Association & Business Software Alliance
This case concerns more than companies that develop large language models (LLMs). It affects medical researchers and patients, educators serving students with different learning needs, people with disabilities seeking greater independence, farmers applying agricultural knowledge, independent creators developing new forms of expression, and industry sectors from software to the law. These communities share an interest in the ability to study and learn from broad and diverse sources of knowledge, and in access to the tools that make that learning useful. Many depend on systems developed by others, including open source AI systems, rather than training models themselves. Fair use supports their ability to learn, discover, communicate, and create. Restrictions on fair use training would affect both the institutions building AI and the people using it.
Authors Alliance
The market dilution theory asks copyright law to protect rightsholders against an economic phenomenon that it has never restricted before. New works can reduce the sales, visibility, or value of older works without copying their protected expression. The Copyright Act deliberately leaves that competition outside the copyright owner’s control. Amicus therefore respectfully asks the Court to hold that “market dilution,” i.e., aggregate competitive pressure from noninfringing outputs, including those that occupy the same subject matter or genre, that share unprotected characteristics, or compete for the same audience, is not cognizable market harm under 17 U.S.C. § 107(4). Rejecting the theory would preserve the distinction the Second Circuit has always maintained: copyright protects authors against substitution for their protected expression, not against the success of new expression that merely competes with it.
Engine and Developers Alliance
Startups’ use of foundation models demonstrates that using copyrighted works to train an AI model is a fair use, not copyright infringement. The purpose of training a foundation model is not to reproduce the works it learns; it is to build a general-purpose tool that others can adapt to new ends. The ends startups have chosen bear no resemblance to the purposes of the works in any training dataset.
Foundation for American Innovation
To the extent AI training enables the creation of new knowledge, expression, software, and other capabilities, those benefits bear on factor four’s assessment of the use’s market effects. That reasoning supports a novel application here: considering the national-security value of capabilities that enable new analysis, software, and other productive work.
Joshua S. Gans
Professor of Strategic Management
Copyright supports not only the incentive to create content but also the incentive to build tools that learn from that content, and a rule that treated every displaced sale as actionable can weaken the second without reliably protecting the first.
Kevin Kelly
Author
AI is a transformative technology that accelerates the creation of novel works across countless disciplines in the arts and sciences. Mr. Kelly has experience in how AI advances the field of literature. In his view, each AI model can be understood as an entirely new medium that offers authors the capability to create new texts and ideas, and to reason. A ruling for Plaintiffs on fair use would therefore grant Plaintiffs far more than the limited monopoly to their original expression Congress intended to confer. It would hinder the development of technologies capable of disseminating previously unimaginable quantities of information. It would also impede new modes of thought and the creation of new original works in both the arts and sciences. Amicus files this brief in support of Defendants’ argument that the use of copyrighted works to train large language models is a fair use under 17 U.S.C. § 107. Based on his lifelong pursuits of writing, the protections afforded to copyright owners are important to Mr. Kelly. Equally vital is preserving room for permissible uses of copyrighted material and the growth of transformative technologies. Otherwise, copyright will stifle the very creativity, innovation, and spread of knowledge it exists to encourage. The fair-use doctrine is critical to ensuring that balance.
Matthew Sag
Professor of Law
On the record in this case, the defendants’ use of plaintiffs’ works for AI training is highly transformative and strongly favors a finding of fair use because under realistic conditions the outputs of defendants’ models are in no relevant way similar to the invisible intermediate copies made during the training process.
Michael Wall and Sarah Rose Siskind
Musician and composer / comedian and writer
This brief offers a window into what that sentence means in practice through the work of two independent creators who rely on copyright protection and AI tools to sustain their livelihoods. Amici Sarah Rose Siskind and Michael Wall are artists who create and license original works. But they use AI to handle work surrounding the creative process and to grow their businesses in a market teeming with better-resourced competitors. For Amici, AI does not displace creativity or shortcut their creative processes. AI helps them create more, and to extend their work into new modes of expression. Amici’s experience underscores the United States’ view that AI tools “can and should help level the playing field between mainstream and independent publishers.” Siskind uses AI in every aspect of her business, from day-to-day administrative tasks to developing ideas and bringing in new clients. With AI, her noncreative work is faster and less expensive, and her creative work is better. As she tells it, “I don’t think I could run my business without AI at this point.” Similarly, AI helps Wall manage the operational demands of his music business, reduce costs, and devote more of his time to composing music. Like Siskind, he doubts that his business could survive in its current form without AI.
Public Knowledge
Fair use has protected tools that help people navigate the vast collections of information the digital world has brought us, including image search and full-text book search. See Perfect 10, Inc. v. Amazon.com, Inc., 508 F.3d 1146, 1164–68 (9th Cir. 2007); Authors Guild v. Google, Inc., 804 F.3d 202, 216–17, 225 (2d Cir. 2015) (“Google Books”). It has also protected libraries’ provision of accessible digital books to readers with print disabilities. See Authors Guild, Inc. v. HathiTrust, 755 F.3d 87, 101–03 (2d Cir. 2014). And it has protected free online publication of technical standards incorporated into law, recognizing the “substantial public benefits of free and easy access to the law.” Am. Soc’y for Testing & Materials v. Public.Resource.Org, Inc., 82 F.4th 1262, 1271–72 (D.C. Cir. 2023). Fair use will be equally beneficial in ensuring that AI’s benefits accrue to the public at large, instead of being controlled by a privileged few.
Ron Suskind and Brian L. Frye
Pulitzer Prize-winning journalist / Professor of Law
OpenAI’s use of newspaper articles to train its generative AI models didn’t harm the reasonable commercial expectations of the News Plaintiffs in any way. They commissioned and published newspaper articles with the expectation of selling consumers access to those articles. They retain and continue to exercise the exclusive right to sell consumers access to their articles. Their ability to sell consumers access to their articles wasn’t affected by OpenAI’s use of those articles to train its AI models.
United States Government
In this litigation, the New York Times seeks to narrow fair-use doctrine to exclude the training of OpenAI’s large language models (LLMs). That result would be inconsistent with basic copyright law principles and severely hamper “the Progress of Science and useful Arts.” U.S. CONST. art. I, § 8, cl. 8. As discussed below, publications—including independent and start-up media, in addition to the New York Times itself—are leveraging LLMs for journalistic and other purposes. And beyond the subject matter of this litigation, LLMs are already helping researchers across fields achieve major breakthroughs. Constraining LLM development under a misunderstanding of fair use doctrine would thwart such creative and scientific progress while hindering American prosperity and economic mobility.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.