The Silicon Reasonable Person: Can AI Predict How Ordinary People Judge Reasonableness?
This addresses the challenge of aligning legal judgments with societal views, offering practical applications for judges, lawmakers, and litigants, though it is incremental in applying existing AI methods to a new domain.
The study investigated whether large language models (LLMs) can predict human reasonableness judgments in legal contexts, finding that certain models capture underlying decisional patterns, prioritizing social cues over economic efficiency in over 10,000 simulated judgments.
In everyday life, people make countless reasonableness judgments that determine appropriate behavior in various contexts. Predicting these judgments challenges the legal system, as judges' intuitions may not align with broader societal views. This Article investigates whether large language models (LLMs) can learn to identify patterns driving human reasonableness judgments. Using randomized controlled trials comparing humans and models across multiple legal contexts with over 10,000 simulated judgments, we demonstrate that certain models capture not just surface-level responses but potentially their underlying decisional architecture. Strikingly, these systems prioritize social cues over economic efficiency in negligence determinations, mirroring human behavior despite contradicting textbook treatments. These findings suggest practical applications: judges could calibrate intuitions against broader patterns, lawmakers could test policy interpretations, and resource-constrained litigants could preview argument reception. As AI agents increasingly make autonomous real-world decisions, understanding whether they've internalized recognizable ethical frameworks becomes essential for anticipating their behavior.