Transformative Use

Transformative use is a core concept in fair use analysis, referring to whether a new work produces new expression, meaning or function based on the original.

In jurisdictions such as the United States, AI companies often invoke "transformative use" to claim fair use, but this defense faces growing challenges in the news content domain.

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

The "transformative use" argument for AI training holds that models do not copy works but rather extract statistical patterns from them.

The first factor of the four-factor test is the purpose and character of the use, i.e., whether it is transformative.

OpenAI argues that the training process constitutes transformative use of large volumes of data, and that the model does not simply copy original text but learns statistical patterns and linguistic structures.

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

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 concept of transformative use originates from the U.S. Supreme Court's 1994 ruling in Campbell v. Acuff-Rose Music.