Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions

2026-07-26

AI Summary

This study addresses the lack of systematic understanding of artificial intelligence–related litigation in U.S. federal courts, which hinders the identification of legal blind spots in AI governance. By conducting a systematic textual review and qualitative coding of 559 federal court opinions, the authors develop the first multidimensional classification framework for AI litigation, identifying seven key dispute domains, six categories of AI technologies involved in lawsuits, and four primary types of litigants. The analysis reveals that courts predominantly rely on existing legal frameworks to adjudicate AI disputes, resulting in a fragmented governance approach that fails to adequately address certain AI-related risks. Furthermore, the study uncovers a significant discrepancy between judicial coverage of AI harms and the incidents documented in current AI incident databases, underscoring the limitations of the judiciary’s responsiveness to emerging AI challenges.

Abstract

In the United States, artificial intelligence (AI) is rapidly deployed amid limited federal regulation. With courts become a recurring forum in which AI-related practices are scrutinized, it is important to empirically understand the AI litigation landscape to date. We address this gap through a systematic review of 559 U.S. federal court opinions in which AI plays a role in the parties' contentions, taxonomizing (1) common topics of dispute, (2) the AI technologies implicated, and (3) the parties involved, including common plaintiff and defendant types. We identify seven recurring dispute areas, six categories of AI technologies at the center of litigation, and four types of common litigants, alongside legal doctrines used by the litigants. A comparison of this taxonomy to the AI Incident Database revealed substantial gaps in coverage, definitions, and prevalence between documented and litigated harms, suggesting courts capture only part of the AI risk landscape. In addition, we found that court decisions primarily rely on pre-existing legal doctrines to manage AI rather than making new AI-specific laws, producing a form of "piecemeal" AI governance. As a result, federal court outcomes are shaped less by where AI has caused harms and more by which harms are cognizable under existing statutes, leading to certain AI harms remaining unresolved.

Problem

• AI litigation
• federal court
• AI governance
• legal doctrines
• AI harms

Innovation

• AI litigation
• federal court opinions
• AI governance
• legal taxonomy
• empirical legal analysis