概述
Judges weigh four factors: the purpose of the use, the nature of the work, how much was copied, and the effect on the market for the original. The first 2025 rulings split. Training was found highly transformative in some generative AI cases, while pirated data and non-generative copying drew liability. The answer affects every AI developer and every creator whose work is in training data.
深入探讨
Section 107 of the Copyright Act lists four factors. Factor one asks whether the use is transformative and commercial. The Supreme Court's 2023 Warhol v. Goldsmith decision emphasized that a different purpose matters more than adding new meaning. Factor two considers whether the work is creative or factual. Factor three looks at how much was taken. Factor four asks whether the use harms the market for the original, and courts often treat it as the most important. The first rulings came in 2025. In February, Judge Stephanos Bibas held in Thomson Reuters v. Ross that Ross's copying of Westlaw headnotes to train a legal search tool was not fair use. Ross was building a direct competitor, and the AI was not generative. The case went to the Third Circuit on interlocutory appeal. In June, Judge William Alsup ruled in Bartz v. Anthropic that training on books was 'exceedingly transformative' and fair use, and that scanning purchased print books was also fair. He held that building a central library from pirate sites was a separate use that fair use did not cover. After class certification, Anthropic agreed to a settlement reported at $1.5 billion, about $3,000 per covered work. Days later, Judge Vince Chhabria ruled for Meta in Kadrey v. Meta, but only because the authors did not develop evidence of market harm. He stressed that 'market dilution' from floods of AI-generated competing works could weigh heavily against fair use in a better-argued case. A common misconception is that courts have declared AI training fair use across the board. These are district court rulings on specific facts. Appeals, other cases and the method of acquiring data all matter.
战略影响
风险与安全
灾难性和日常的人工智能危害都取决于谁了解风险以及谁能够采取行动。
更清晰的判决
公众和专业素养决定强有力的安全政策在政治上是否可行。
打破炒作
清晰的解释可以减少炒作、实验室公关和模糊道德剧场的影响。
The Future of Fair Use and AI Training Data
Appellate decisions, including the Third Circuit's review of Thomson Reuters v. Ross, are likely to shape the doctrine more than any single trial ruling. Expect courts to keep separating how data was acquired from how it was used, and to look closely at market harm evidence. Licensing deals will probably keep growing, since they reduce legal risk regardless of outcome. Congress could legislate, and the Copyright Office has published its own analysis of generative AI training. Until then the law remains unsettled and depends on the facts of each case.
现实世界的实施
In Bartz v. Anthropic, a judge found training Claude on lawfully purchased and scanned books was fair use, but downloading millions of pirated books into a library was not excused.
In Thomson Reuters v. Ross Intelligence, a court rejected fair use for a legal research startup that used Westlaw headnotes to build a competing, non-generative search tool.
In Kadrey v. Meta, authors lost at summary judgment because they did not prove market harm, even though the judge suggested such harm could exist in other cases.
A news publisher signs a paid licensing deal with an AI company, which both earns revenue and supports arguments that a training license market exists.
风险与防护栏
将存在风险视为科幻小说，同时能力复合。
混淆了表面产品安全与高度自治下的对准。
只给非英语和非专业观众留下低质量的资源。
实施路线图
单独的产品危害、误用和失控/失调风险。
询问哪些证据会改变您对时间表和严重性的看法。
比起营销主张，更喜欢主要来源和具体评估。
确定一条行动路径：职业、政策、资金或技能——而不仅仅是意识。
不断探索
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常见问题
What is Fair Use and AI Training Data?
Fair use is the US copyright doctrine that courts are using to decide whether training AI models on copyrighted works without permission is lawful. Judges weigh four factors: the purpose of the use, the nature of the work, how much was copied, and the effect on the market for the original. The first 2025 rulings split. Training was found highly transformative in some generative AI cases, while pirated data and non-generative copying drew liability. The answer affects every AI developer and every creator whose work is in training data.
Which fair use factor asks about harm to the market for the original work?
Factor four considers the effect on the potential market for or value of the original, and courts often treat it as the most important.
Why did the court reject fair use in Thomson Reuters v. Ross Intelligence?
Judge Bibas found Ross's use was not transformative enough and served as a direct market substitute for Westlaw.
In Bartz v. Anthropic, what did Judge Alsup find was NOT covered by fair use?
Alsup held training and scanning lawfully bought books were fair use, but acquiring and keeping pirated books in a library was a separate, unexcused use.
What settlement did Anthropic reach in Bartz, as reported?
After class certification, the reported settlement was about $1.5 billion, approximately $3,000 per covered work.
Why did Meta win in Kadrey v. Meta?
Judge Chhabria ruled narrowly because the plaintiffs failed to show market harm, while suggesting a better-argued case could succeed.
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