Fair Use

The core dispute in AI copyright litigation is whether the large-scale ingestion of copyrighted works during the AI model training phase constitutes "fair use"; proponents cite the Google Books case as precedent, while opponents point out that AI models can sometimes reproduce training data nearly verbatim.

Fair use is a principle in U.S. copyright law that permits limited use of copyrighted works without the copyright holder's permission for purposes such as commentary, news reporting, teaching, and research.

The fair use doctrine established under Section 107 of the U.S. Copyright Act was codified in 1976, and courts must weigh four factors: the purpose and character of the use, the nature of the copyrighted work, the amount used, and the market impact.

A court's assessment of fair use must consider four factors: the purpose and character of the use, the nature of the copyrighted work, the amount of the original work used, and the effect on the market for the original work.

Fair use analysis under U.S. copyright law requires weighing four factors, with the transformativeness of the use and the impact on the original work's market being the key points of dispute.

AI companies argue that model training constitutes 'transformative use,' but rights holders counter that the training process involves complete copying of original works, and that generative AI is directly competing with and substituting for the original works market.

The Fair Use doctrine established by Section 107 of the U.S. Copyright Act is the primary defense basis for AI companies, with the core argument being that model training constitutes transformative use.

合理使用是美国版权法第107条确立的例外原则，判断标准涵盖使用目的、作品性质、使用比例及对市场的影响四个维度

If courts rule that AI training constitutes copyright infringement, legitimate activities such as search engine web indexing and caching, text mining, and large-scale data analysis in academic research could all be implicated

OpenAI maintains that its model training falls within the scope of Fair Use.

The fair use doctrine can be traced back to the case law rule established by federal judge Joseph Story in the 1841 case Folsom v. Marsh, and was later codified in Section 107 of the U.S. Copyright Act in 1976.

The four factors of fair use in U.S. copyright law (purpose of use, nature of the work, amount used, and market impact) remain undetermined in the context of AI training.

Whether AI training constitutes infringement has no unified conclusion globally, and different jurisdictions vary greatly in their definitions of fair use and text and data mining exceptions.

Transformative use precedent holds that if a new work substantially transforms the original in purpose and character rather than simply substituting for it, it is more likely to be deemed fair use.

There remains significant debate in academia over whether AI model training constitutes 'fair use'.

The United States handles AI training data under the 'fair use' doctrine, in a relatively ambiguous manner.

The fair use determination under U.S. copyright law is based on a four-factor test: the purpose and character of the use, the nature of the copyrighted work, the amount and substantiality of the portion used, and the effect on the potential market.

Factual works enjoy less copyright protection than creative works under the four-factor test

Using copyrighted commercial recordings as reference input for AI music generation may give rise to legal disputes, as existing copyright frameworks have yet to establish clear rules for this.

AI companies such as OpenAI typically invoke the 'fair use' principle as a defense, arguing that training models constitutes 'transformative use'.