法官变量：挑战法官无关的法律判决预测
The Judge Variable: Challenging Judge-Agnostic Legal Judgment Prediction
AI总结：
该研究通过机器学习预测法国上诉法院儿童监护权裁决，证明个体法官身份显著影响案件结果，专家模型预测准确率优于法官无关的通用模型，为法律现实主义提供了实证支持。
摘要：
本研究通过使用机器学习预测法国上诉法院中儿童实际监护权结果，考察人类法官在法律决策中的作用。在法律现实主义与法律形式主义之争的基础上，我们检验了个体法官的决策模式是否显著影响案件结果，从而挑战法官是统一适用法律的中性变量这一假设。为确保符合法国隐私法律，我们实施了严格的假名化处理流程。我们的分析使用了从10,306起案件中提取的18,937项生活安排裁定。我们将基于个体法官过往裁定训练的模型（专家模型）与基于聚合数据训练的法官无关模型（通用模型）进行比较。预测流程采用混合方法，结合大语言模型（LLMs）进行结构化特征提取，以及机器学习模型进行结果预测（RF、XGB和SVC）。结果表明，专家模型始终比通用模型取得更高的预测准确率，表现最佳的模型F1分数高达92.85%，而通用模型在训练样本多20倍至100倍的情况下F1分数为82.63%。专家模型捕捉到稳定的个体模式，这些模式无法迁移到其他法官。域内和跨域有效性检验为法律现实主义提供了实证支持，表明法官身份在法律结果中发挥着可测量的作用。所有使用的数据和代码将予以公开。
英文摘要：
This study examines the role of human judges in legal decision-making by using machine learning to predict child physical custody outcomes in French appellate courts. Building on the legal realism-formalism debate, we test whether individual judges' decision-making patterns significantly influence case outcomes, challenging the assumption that judges are neutral variables that apply the law uniformly. To ensure compliance with French privacy laws, we implement a strict pseudonymization process. Our analysis uses 18,937 living arrangements rulings extracted from 10,306 cases. We compare models trained on individual judges' past rulings (specialist models) with a judge-agnostic model trained on aggregated data (generalist models). The prediction pipeline is a hybrid approach combining large language models (LLMs) for structured feature extraction and ML models for outcome prediction (RF, XGB and SVC). Our results show that specialist models consistently achieve higher predictive accuracy than the general model, with top-performing models reaching F1 scores as high as 92.85%, compared to the generalist model's 82.63% trained on 20x to 100x more samples. Specialist models capture stable individual patterns that are not transferable to other judges. In-Domain and Cross-Domain validity tests provide empirical support for legal realism, demonstrating that judicial identity plays a measurable role in legal outcomes. All data and code used will be made available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
