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arXiv · 2611.04921

PROSE: Preservation-Constrained Selective Editing for Guilty Bias in Legal Judgment Prediction

Yimeng Wang·Zeyuan Tian·Xinyu Wang·Danyang Gao·Chenglei Shen·Zhongxiang Sun·Lei La·Weijie Yu
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Abstract

Legal judgment prediction models may retain a guilty verdict after a legally decisive condition has been altered such that the specified charge is no longer established. We study this guilty bias via selective repair over matched charge-established and charge-not-established cases, where the unsupported verdict should change while its supported counterpart remains stable. We construct CAIL-MC, a legally reviewed dataset of 11,478 matched pairs covering 261 Chinese criminal charges. Held-out activation interventions show that contrasts learned from correctly resolved pairs transfer to false-guilty cases, but target-reaching interventions can also damage matched supported-guilty decisions. Motivated by this observation, we introduce PROSE, which reconstructs case-specific targets from a training-derived residual geometry, aligns them with a local margin-descent signal, attenuates supported-guilty-sensitive components, and realizes bounded targets through a preservation-regularized low-rank parameter update. Across Qwen3-8B and GLM4-9B, PROSE achieves stronger pairwise safe repair and evidence sensitivity, while preserving standard single-pass inference.

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Yimeng Wang, Zeyuan Tian, Xinyu Wang, Danyang Gao, Chenglei Shen, Zhongxiang Sun, Lei La, Weijie Yu. 2026-11-03. PROSE: Preservation-Constrained Selective Editing for Guilty Bias in Legal Judgment Prediction. https://arxiv.org/abs/2611.04921

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