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

arXiv: https://arxiv.org/abs/2607.23888 · HTML: https://mlsanity.com/paper/2607.23888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice](https://mlsanity.com/paper/2402.01864/) (2024)
- [Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks](https://mlsanity.com/paper/2310.07879/) (2023)
- [LexNLP: Natural language processing and information extraction for legal and regulatory texts](https://mlsanity.com/paper/1806.03688/) (2018)
- [Pitfalls of Evidence-Based AI Policy](https://mlsanity.com/paper/2502.09618/) (2025)
- [Automated Transparency: A Legal and Empirical Analysis of the Digital Services Act Transparency Database](https://mlsanity.com/paper/2404.02894/) (2024)
- [Comparing Apples to Oranges: A Taxonomy for Navigating the Global Landscape of AI Regulation](https://mlsanity.com/paper/2505.13673/) (2025)
- [Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPA](https://mlsanity.com/paper/2205.07299/) (2022)
- [Regulating AI: Where U.S. State Policy and HCI (Mis)align](https://mlsanity.com/paper/2607.03292/) (2026)

## Abstract (arXiv)

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.