用辩护律师角色在推理中对抗有罪偏见，提升法律判决预测公正性。
OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction
- 引入对抗性辩护律师代理，在每步推理中主动质疑有罪假设。
- 将虚假有罪率从82.93%降至16.69%，显著降低误判。
- 基于真实案件构建新数据集，适合关注司法AI伦理的研究者。
法律判决预测模型通常基于检方视角的案情文档训练，现有数据集也严重偏向有罪结果，导致模型产生‘有罪偏见’，盲目接受检方叙事为客观事实。尽管已有研究通过三步推理结构或合成无罪数据提升准确率，但无法在推理阶段有效缓解偏见。本文提出OBJECTION，一种在推理时集成对抗性律师代理的流程，嵌入于‘行为、违法性、责难性’三步推理中。该代理在每一步主动注入辩护论点，挑战模型对有罪的预设。为全面评估，我们构建了包含3.4k真实案例的‘自然无辜’数据集，克服了合成无罪基准的局限。测试结果显示，OBJECTION将虚假有罪率（FGR）从82.93%（最先进基线）降至16.69%，证明其具备实质性的法律推理能力。本工作标志着法律人工智能向‘无罪推定’原则迈进关键一步。
原文摘要 · Abstract (English)
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.
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