JudgmentLens: Human-AI Sensemaking of Complex Legal Judgments

2026-09-07

AI Summary

研究通过开发JudgmentLens系统，利用AI增强阅读功能解决非专家难以解读复杂法律判决的问题。

Abstract

Judicial judgments are increasingly available, yet dense language and distributed relationships among facts, evidence, reasoning, and rulings remain difficult for non-experts to interpret. Through a mixed-methods formative study with Chinese non-expert readers (survey N=34; interviews N=6), we identified structural, interpretive, verification, and action breakdowns. We developed JudgmentLens, an AI-augmented reading system combining persistent case representations, adaptive explanations, and traceable links from generated interpretations to judgment passages. In a counterbalanced within-subject evaluation (N=16), participants completed tasks faster with JudgmentLens than with conventional PDF reading and reported lower workload and greater self-reported decision understanding, while rubric-scored comprehension did not differ reliably. An exploratory PDF+DeepSeek probe suggested that conversational AI supported formulated questions while leaving question formulation, answer integration, and source checking largely to users. We contribute an empirical account of non-expert judgment sensemaking and design strategies for inspectable, source-grounded AI mediation.

Problem

• legal judgments
• non-expert readers
• complex language
• distributed relationships

Innovation

• AI-augmented reading system
• persistent case representations
• adaptive explanations
• traceable links