OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction
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 culpab...
| Authors | Jaehoon Jeong, Jay-Yoon Lee |
|---|---|
| Primary category | cs.CL |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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