STACKING THE DECK: AI, JURY SELECTION, AND THE NEW BATSON PROBLEM

Authors

• Alexandria Serra

Abstract

Generative artificial intelligence has infiltrated jury selection right under our noses. What began as a passive data mining experiment has evolved into AI-powered jury selection tools that profile potential jurors by scoring, ranking, and recommending which citizens to exclude from jury service. Although these tools promise objectivity, they risk encoding the same racial and gender biases that Batson v. Kentucky sought to eliminate. Courtroom AI is here to stay. But when lawyers delegate peremptory strikes to statistical models trained on biased data, Batson becomes obsolete. No court can detect algorithmic bias that lacks discernible intent. No judge can analyze whether there is race-neutral reasoning behind a strike when a proprietary system obscures its reasoning altogether.

This Article exposes how AI makes discrimination in jury selection more sophisticated and less detectable. Specifically, it examines how large language models (LLMs)—the AI systems that power tools like ChatGPT—are trained on massive datasets that reflect and amplify social biases. Deploying these systems in jury selection means that historical patterns of discrimination drive strike decisions at scale. Through analysis of commercial platforms, the Article demonstrates that AI-powered jury selection tools rely on facially neutral factors that correlate with race: zip codes become proxies for ethnicity, surnames trigger discriminatory associations, and linguistic patterns encode class and cultural bias. Worse, testing reveals that these systems coach users to circumvent Batson challenges entirely.

This Article does not call for banning AI in jury selection. AI can democratize sophisticated analytics previously available only to elite litigators and widen access for public defenders and firms with fewer resources. But without prescriptive safeguards, these tools will entrench discrimination rather than reduce it. Courts must adopt objective strike-pattern standards to replace Batson's intent-based framework, expand cause challenges to reduce reliance on peremptories, and require auditing of AI jury selection systems. Preserving the Sixth Amendment right to an impartial jury requires human judgment that algorithms can inform but never replace.

Recommended Citation

Alexandria Serra, STACKING THE DECK: AI, JURY SELECTION, AND THE NEW BATSON PROBLEM, 21 Wash. J. L. Tech. & Arts (2026).
Available at: https://digitalcommons.law.uw.edu/wjlta/vol21/iss3/3