Fair Use Falls Short: Judge Bibas Rejects AI Training Data Defense in Thomson Reuters v. ROSS

Judge Stephanos Bibas recently reversed his earlier stance in a pivotal ruling against an AI company, significantly narrowing the application of fair use in copyright litigation. By determining that the core questions regarding transformative use were matters of law rather than fact, he denied the defendant the ability to rely on this defense. This decision marks a critical turning point, signaling that courts are less likely to accept the argument that AI training constitutes fair use when the resulting technology serves the same commercial purpose as the original copyrighted material. The ruling emphasizes that merely creating a new platform does not constitute transformative use if it competes directly with the source work. Unlike previous precedents where copying was technically necessary for interoperability, the AI company in question had the option to create its own data but chose to utilize existing proprietary structures for expediency. Consequently, the court found that the AI’s output fulfilled the same function as the original content, failing the primary test for fair use and highlighting the legal risks of using copyrighted works to train competing commercial products. This case is vital to the open data movement as it challenges the assumption that machine learning processes inherently justify the unauthorized use of copyrighted datasets. It suggests that the "transformative" defense may no longer protect AI developers who train models on protected content to build competing services. As a result, the legal landscape is shifting toward mandatory licensing agreements, forcing data users to negotiate rights upfront rather than relying on broad fair use interpretations. This establishes a stricter precedent for data accessibility, potentially requiring more formalized and compensated access to training materials for future AI development.

Source: natlawreview.com
Published on 2025-02-19

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