Copyright Litigation Ruling Spotlights Applicability of Fair Use as a Defense for AI Training

In a recent twist to a closely followed AI-related copyright infringement case between two legal research software providers, a federal court reversed its prior rulings and held the company accused of copyright infringement could not maintain its “fair use” and other defenses.

The court initially denied the parties’ cross-motions for summary judgment in September 2023, citing factual issues as to both whether the headnotes were sufficiently original to warrant copyright protection, as well as factual issues on elements of a fair use defense. Before the scheduled trial date in August 2024, the court continued the trial date and requested the parties to submit additional briefing.

After finding the defendant directly copied over 2,000 “headnotes,” or summaries of legal opinions, and that the headnotes were sufficiently original and copyrightable, the court spent most of its opinion focused on the defendant’s claim that the copying of data to train AI was excused under the fair use defense. The court considered the four factors that are weighed when assessing a claim of fair use: the (1) purpose and character of the use, (2) nature of the copyrighted work, (3) amount and substantiality of the portion used, and (4) effect of the use upon the potential market for or value of the copyrighted work.

While the court found that the second and third factors weighed in the defendant’s favor, because the headnotes were not as original or creative as fictional works and the defendant’s AI product did not directly reproduce the copied headnotes, the court found that the two most important factors weighed in the plaintiff’s favor. Specifically, the court found that the purpose of the challenged use was commercial, and that the defendant intended to compete directly with the incumbent plaintiff from the legal research market and enter the market of providing AI-powered legal research tools.

The court also noted that it did not matter if the plaintiff also intended to train its own AI tools on its headnotes, as the effect on the potential market was enough for the fourth factor to weigh in the plaintiff’s favor.

Importantly, the court took care to distinguish the AI tools in question from generative AI tools. The court found it important that the AI tools being challenged were trained on copyrighted works and only returned relevant judicial opinions based on its training to a user’s queries—not generate new output in response to prompts. The court explicitly left open the question of whether the fair use defense could succeed where generative AI tools are at issue and whether generative AI would change the analysis of a direct competitor creating content from an incumbent’s content.

Takeaways

This ruling reinforces copyright protections for curated legal research materials and signals stricter scrutiny for AI-driven data scraping in competitive industries. Additionally, it highlights a growing legal challenge for AI developers relying on proprietary datasets for training models.