Don’t count on courts to rein in unregulated AI

Over the past few months, the headlines might give the impression that the U.S. court system is deftly navigating the world of tech regulation. A federal judge quickly enjoined the Department of Defense’s overbroad and retaliatory attempt to label artificial intelligence (AI) developer Anthropic a supply chain risk. Juries in California and New Mexico issued major verdicts against Meta and Google for harms arising from their social media products, in cases that could influence thousands of other similar claims. And the U.S. Supreme Court let stand a lower court’s decision over the copyrightability of AI-generated art, in one of the first actions by the Court in the area of intellectual property (IP) and artificial intelligence. Based on this flurry of activity, one might think that when it comes to the current greatest challenge for technology policy—artificial intelligence—the courts can be trusted to man the regulatory helm.

But that trust would be misplaced. We are years into the explosive growth of the generative AI industry, and its integration into commerce and daily life has raised many important legal questions. But the courts have largely failed to resolve these issues in a timely enough way to inform the development of this major sector. The state of AI litigation over the past few years raises concerns about the adequacy of courts and their procedures in this fast-changing area, and suggests that policymakers may need to think twice about relying on courts as cornerstones of new regulatory regimes. 

The “ChatGPT moment” of late 2022 is now more than three years ago. Even at that early time, a number of important legal questions were fairly obvious. Among the most significant ones: Is it a violation of copyright protections to train on copyrighted data? Are the outputs of AI tools copyrightable? If a user causes a tool to generate outputs that violate IP protections, who is liable—the user? The developer? Both? Neither? Many of these major early questions focused on intellectual property. And some of them continue to have implications for the viability of large parts of the industry. If it is not fair use to train large language models on copyrighted data, for instance, it’s not clear that the current paradigm of training on massive text corpuses would continue to be feasible.

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