OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction

2026-09-02

AI Summary

本文针对法律判决预测中的'有罪偏见'问题，提出了一种名为OBJECTION的方法，通过在每个推理阶段引入对抗律师代理来注入辩护论点，显著降低了错误的有罪率。

Abstract

Legal Judgment Prediction (LJP) models are typically trained on documents that describe facts from a prosecutorial perspective. Existing datasets further exhibit severe label imbalance toward guilty outcomes. Consequently, these models suffer from "Guilty Bias", blindly accepting the prosecution's narrative as objective truth. Previous studies employing three-step reasoning structures or training on synthetically generated innocence data improve overall accuracy, but they still fail to mitigate bias at inference time. In this paper, we introduce OBJECTION, an inference-time pipeline that integrates an Adversarial Lawyer Agent into each 3-step reasoning of offense, unlawfulness, and culpability. Unlike generic critics, our agent actively challenges the model's presumptions of guilt by injecting legal defense arguments at each reasoning stage. To thoroughly evaluate this, we present a new "Natural Innocent" dataset including 3.4k real-world cases, overcoming the limitations of synthetic innocence benchmarks. Test results show that OBJECTION drastically reduces the False Guilty Rate (FGR) from 82.93% (SOTA baseline) to 16.69%, proving its capability to perform substantive legal reasoning. This work denotes a key progress toward aligning Legal AI with the presumption of innocence.

Problem

• Legal Judgment Prediction
• Guilty Bias
• Label Imbalance

Innovation

• Adversarial Lawyer Agent
• OBJECTION Pipeline
• Natural Innocent Dataset
• False Guilty Rate Reduction