AI fair use ruling limits copyright defenses

By Kris Olson & Kallie Cox

They are far from the last words a judge will write on the subject, nor do they have any precedential value. But that has not stopped attorneys from poring over a recent Delaware federal court decision that represents the first substantive application of copyright law and fair use doctrine to artificial intelligence.

The case, Thomson Reuters Enterprise Centre GMBH, et al. v. Ross Intelligence Inc., involves an AI-powered legal research search engine that the defendant created to compete with Westlaw, a product of plaintiff Thomson Reuters.

Rebuffed when it asked to license Westlaw’s content, Ross Intelligence instead struck a deal with the company LegalEase to get access to LegalEase’s “Bulk Memos” to use as training data for its AI. Bulk Memos are lawyers’ compilations of legal questions with good and bad answers. They were created using Westlaw headnotes, which summarize key points of law and case holdings. The lawyers hired by LegalEase were told they should use Westlaw’s headnotes, but they should do more than just copy and paste them directly into the questions they were writing.

Back in 2023, 3rd U.S. Circuit Court of Appeals Judge Stephanos Bibas, sitting by designation in Delaware, largely denied Thomson Reuters’ motions for summary judgment on the issues of copyright infringement and Ross’ fair use defense.

But as the case was moving toward trial last August, Bibas “studied the case materials more closely and realized that my prior summary-judgment ruling had not gone far enough.” He continued the trial and invited the parties to renew their summary judgment briefing. On Feb. 11, Bibas issued an opinion reversing himself on both fronts.

William Y. Klett III, who practices at Burr & Forman in Charleston and Columbia, South Carolina, said the fair use defense was rejected because of the use of Westlaw’s headnotes.

“Everyone knows that the actual opinions generated in court cases are not subject to copyright protection. But what Westlaw had done was they had gone a step beyond that with their keynote system and the head notes,” Klett said. “So, their selection and arrangement of the topics within each reported decision, that was all original to Westlaw and Thomson Reuters.”

In his ruling, Bibas made it a point to highlight the fact that “only non-generative AI is before me today.”

That fact may limit the persuasive effect of the holding in cases like the one brought by the New York Times against OpenAI and Microsoft, alleging that the newspaper’s copyrights were infringed in training ChatGPT and Copilot.

The Thomson Reuters case is not necessarily representative of generative AI and large language models, said Bryan L. Baysinger of Maynard Nexsen, who practices in North Carolina and South Carolina. Instead, it involves what he called “traditional AI.” Traditional AI focuses on analyzing and predicting data, whereas generative AI creates new data.

Another distinction between the Thomson Reuters and New York Times cases is that the fair use issue was a factor in the former.

Baysinger notes that Ross is a direct competitor of Thomson Reuters.

“[With] Open AI, it’s hard to say that they’re going to be a direct competitor to the New York Times,” Baysinger said. The Times could make the argument that journalists will lose page views and instead will read summaries of articles produced by AI, but that may be difficult to prove, he added.

“The Ross case is good. But it’s not really on generative AI. So, I think the real test, and what the world is looking at, is what’s going to happen with this New York Times case,” Baysinger said.

Charles Raphun, a partner with Cranfill Sumner in Raleigh, agreed that the Thomson Reuters case is different.

Assuming the ruling is appealed, Raphun suggested the focus will be more on traditional copyright and technology issues, rather than the use of AI.

Like a sculptor

As a threshold matter in the Thomson Reuters case, Bibas had to decide whether Westlaw’s headnotes were copyrightable at all, given that they are drawn from an underlying judicial opinion they are summarizing, often word for word.

In 2023, the judge had used that characteristic to deny Thomson Reuters’ motion for summary judgment.

His change of heart on whether the headnotes were copyrightable became clear once he analogized the editorial judgment of a lawyer writing a headnote to that of a sculptor, Bibas explained.

“A block of raw marble, like a judicial opinion, is not copyrightable,” Bibas wrote. “Yet a sculptor creates a sculpture by choosing what to cut away and what to leave in place. That sculpture is copyrightable. So too, even a headnote taken verbatim from an opinion is a carefully chosen fraction of the whole. Identifying which words matter and chiseling away the surrounding mass expresses the editor’s idea about what the important point of law from the opinion is.”

Not like computer code

Having established that Westlaw’s headnotes were copyrightable, Bibas moved on to reconsider whether any defenses to infringement applied, including fair use.

Once again, Bibas reversed himself. Looking at the four-factor fair use test, Bibas gave two wins to each side. But the more important factors — the purpose and character of Ross’ use of Thomson Reuters’ copyrighted material and how Ross’ use affected the copyrighted work’s value or potential market — tipped in Thomson Reuters’ favor.

Initially, Bibas had bought into Ross’ argument that it had merely engaged in a permissible act of “intermediate copying,” which a series of cases analyzing computer programs had found to be fair use.

Upon further reflection, Bibas realized that copyright law recognizes that “computer programs differ from books, films, and many other literary works in that such programs almost always serve functional purposes.”

In addition, the computer programming cases about intermediate copying rely on a factor not present in Thomson Reuters: “The copying was necessary for competitors to innovate,” Bibas wrote.

For example, in the 2021 U.S. Supreme Court case Google LLC v. Oracle Am., Inc., Google had copied part of a computer programming language “necessary for different programs to speak to each other,” Bibas wrote.

“Here, though, there is no computer code whose underlying ideas can be reached only by copying their expression,” Bibas found. “The ‘copying is [not] reasonably necessary to achieve the user’s new purpose.’”

Market effects

With respect to the test’s fourth factor, which he noted was of paramount importance, Bibas said he was required to consider not only current markets but potential derivative ones developed either by content creators or their licensees. The Supreme Court in Google had also directed him to consider the public benefits the copying was likely to produce, he added.

Bibas’ previous decision hinged on the possibility that Ross might create “a brand-new research platform that serves a different purpose than Westlaw.”

“If that were true, then Ross would not be a market substitute for Westlaw,” he wrote.

In addition, he thought there was a relevant, genuine issue of material fact about whether Thomson Reuters would use its data to train AI tools or sell its headnotes as training data. If not, the public’s interest might be better served by protecting a “copier” rather than a creator.

However, Bibas had come to believe those concerns were “unpersuasive.”

Ross meant to compete with Westlaw by developing a market substitute, he wrote.

“And it does not matter whether Thomson Reuters has used the data to train its own legal search tools; the effect on a potential market for AI training data is enough,” Bibas said.

While there might be a public interest in accessing the law, “the public has no right to Thomson Reuters’s parsing of the law,” he wrote.

One of the looming questions surrounding issues of intellectual property law as it pertains to AI is whether or not copyrighted material that is used to train large language models is transformative enough to constitute the fair use of the material.

In the Thomson Reuters case, what was notable was that Bibas found that Ross’ use was not transformative as a matter of law.

Raphun noted a Supreme Court opinion cited in one of the briefs in the case, Andy Warhol Foundation for the Visual Arts, Inc.v. Goldsmith, which “seemed to reduce the focus that a court makes on transformativeness. The case indicated that you need to focus more on the objective aspects of the use of the creative work that is supposedly infringing and so you look at what it was using, was it commercial, the reasons for copying — things like that.”

If this case makes it to the Supreme Court, Raphun said he believes the justices would use the Warhol case as a guidepost for their decision.

“I think they would probably downplay the significance of the technology tool as far as whether [there] was a transformation in the use of the information, and they would just look what was the defendant doing?” Raphun said. “They would probably just say, ‘Well, Ross is a competitor of Thomson Reuters. They were using the output in a very similar way to compete with Thomson Reuters.”

As these cases continue to wind their way through the courts, attorneys are warning clients to ensure they have the rights to any material they are using to train their large language models.

“The real key is to understand the content that’s being used to develop that model, to ensure that they have rights to that corpus,” Baysinger said. “We’re advising clients that if they are to use an AI model, to use it through an API with a license that grants them the rights to use the content and shifts the burden to that company.”