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

Research from arXiv explores the use of Large Language Models (LLMs) to simulate juror decision-making, specifically examining how defendant statements influence LLM-simulated jurors. The study investigates factors such as persuasion, ideological bias, and background-based affinity in common-law jury trials. It introduces 'JuryBench,' a benchmark comprising controversial U.S. criminal cases, to test varied defendant backgrounds and courtroom statements (emotional appeal, rebuttal) against diverse ideological juror profiles.

This research provides insights into the potential for artificial intelligence to model and predict human decision-making in high-stakes environments. Understanding how simulated jurors are influenced can inform future developments in legal technology, bias mitigation, and the strategic presentation of information in various decision-making contexts.

LLMs are being employed to simulate human decision-making, including complex scenarios like jury trials.

Forward consideration, not a verified fact.