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When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors

arXiv 2609.09887 ↗First posted 9 Sep 2026cs.CL cs.CYcited by 0

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Abstract (arXiv)

LLMs have been used to simulate human decision-making in professional settings, yet their behaviors in common-law jury trials remain unexplored. We study when and how a defendant's courtroom statement affects LLM-simulated jurors, focusing on persuasion, ideological bias, and background-based affinity. To support the analysis, we introduce JuryBench, a benchmark containing controversial criminal cases in U.S. criminal law. In each case, a defendant can claim various plausible justifications to support acquittal or reduced liability. We fix the base case and design defendants of different backgrounds, who give courtroom statements with varying emotional appeal or rebuttal. Jurors with diverse ideological profiles across the spectrum are simulated. We examine 20 frontier LLMs, resulting in a total of 432K decisions and rationales, and quantify changes in verdict severity. Our findings show that LLM-jury simulation echoes many human-jury findings. First, emotional persuasion can be detrimental, since jurors may perceive it as evidence of guilt or inconsistency. Next, we show that background fit between jurors and defendants is a stronger and significant factor than other isolated factors, and that jurors are in general harsher toward opposite-background defendants and lenient toward same-background ones. Finally, we find that juror ideology also strongly shapes severity judgments. These findings highlight both the promise and risks of using LLMs to model jury reasoning and call for careful evaluation. The data and code are available at https://github.com/choyingw/JuryBench

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 64 references that are in the map. First posted on arXiv 9 Sep 2026.

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References · 23

  1. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
  2. 2021LexGLUE: A Benchmark Dataset for Legal Language Understanding in Englishcited by 302
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  5. 2024Simulating Classroom Education with LLM-Empowered Agentscited by 125
  6. 2024AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulatorcited by 99
  7. 2023SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrievalcited by 61
  8. 2022Legal Prompt Engineering for Multilingual Legal Judgement Predictioncited by 54
  9. 2022A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Predictioncited by 44
  10. 2025Self-Evolving Multi-Agent Simulations for Realistic Clinical Interactionscited by 40
  11. 2024AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentationcited by 39
  12. 2024AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agentscited by 36
11 more
  1. 2025PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactionscited by 30
  2. 2024LLMs Can Simulate Standardized Patients via Agent Coevolutioncited by 22
  3. 2025Multi-Agent Simulator Drives Language Models for Legal Intensive Interactioncited by 22
  4. 2022ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the UScited by 17
  5. 2024Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environmentscited by 16
  6. 2025GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citationscited by 15
  7. 2026A Survey on LLM-based Conversational User Simulationcited by 14
  8. 2025Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMscited by 9
  9. 2025TalkDep: Clinically Grounded LLM Personas for Conversation-Centric Depression Screeningcited by 6
  10. 2025Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Stylecited by 4
  11. 2025AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Predictioncited by 3

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