The New Pro Se: Generative AI and the Surge in Federal Civil Self-Representation

2026-05-28

AI Summary

This study examines the impact of generative AI adoption on pro se litigation in U.S. federal civil courts. Leveraging a dataset of approximately 2.8 million complaints filed between fiscal years 2008 and 2025, the authors develop the first interpretable metric for consistent AI-assisted drafting and combine econometric robustness checks, stylometric analysis, and AI detection techniques to identify AI-generated pleadings. Findings indicate that the pro se filing rate rose from 11.33% to 16.94%, with 13.9% of non-form complaints exhibiting strong indicators of AI generation. These AI-assisted pleadings display higher citation density, increased representation among first-time filers and women, yet face higher dismissal rates without improving plaintiffs’ success outcomes, thereby imposing greater initial screening burdens on courts.

Abstract

Since public access to generative AI tools became widespread, federal civil litigation has seen a marked increase in pro se (self-represented) plaintiffs. This paper analyzes that shift using ~2.8 million filings, asking whether the post-GenAI period is associated not only with more pro se filings, but also with detectable changes in complaint text, litigation outcomes, and the composition of pro se litigants. Using civil filing data from FY2008-2025, we find that the federal civil pro se plaintiff rate rose from 11.33% pre-GenAI to 16.94% post-GenAI, a 5.61 percentage-point increase that persists after trend and covariate-adjusted robustness checks. We then focus on Civil Rights and Other Statutory cases, where the increase is especially pronounced, and link case metadata to pro se complaints. Drawing on stylometric AI detection indicators, we develop an interpretable measure of AI-consistent drafting. Against a threshold calibrated to the pre-GenAI baseline, the net AI-flagged share is 13.9% of post-GenAI non-form complaints. Analysis of the AI-flagged complaints shows that they are more citation-dense, disproportionately associated with first-time rather than repeat filers, and geographically unevenly distributed. This composition pattern suggests that AI-consistent drafting is not merely a repeat-filer phenomenon; it also includes a modest, suggestive increase in name-inferred female plaintiffs. We find no evidence of improved win rates; in fact, AI-flagged complaints are more likely to be dismissed and to terminate at earlier procedural phases. These findings raise new questions about access to justice and court screening burdens, and sharpen the distinction between legal formality and legal efficacy.

Problem

• generative AI
• pro se litigation
• federal civil litigation
• access to justice
• AI-assisted drafting

Innovation

• generative AI
• pro se litigation
• stylometric detection
• legal drafting
• access to justice