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arXiv · 2509.12104

JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference

Zongyue Xue·Siyuan Zheng·Shaochun Wang·Yiran Hu·Shenran Wang·Yuxin Yao·Haitao Li·Qingyao Ai·Yiqun Liu·Yun Liu·Weixing Shen
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Abstract

The integration of Large Language Models (LLMs) into legal practice raises pressing concerns about judicial fairness, particularly due to the nature of their "black-box" processes. This study introduces JustEva, a comprehensive, open-source evaluation toolkit designed to measure LLM fairness in legal tasks. JustEva features several advantages: (1) a structured label system covering 65 extra-legal factors; (2) three core fairness metrics - inconsistency, bias, and imbalanced inaccuracy; (3) robust statistical inference methods; and (4) informative visualizations. The toolkit supports two types of experiments, enabling a complete evaluation workflow: (1) generating structured outputs from LLMs using a provided dataset, and (2) conducting statistical analysis and inference on LLMs' outputs through regression and other statistical methods. Empirical application of JustEva reveals significant fairness deficiencies in current LLMs, highlighting the lack of fair and trustworthy LLM legal tools. JustEva offers a convenient tool and methodological foundation for evaluating and improving algorithmic fairness in the legal domain.

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Zongyue Xue, Siyuan Zheng, Shaochun Wang, Yiran Hu, Shenran Wang, Yuxin Yao, Haitao Li, Qingyao Ai, Yiqun Liu, Yun Liu, Weixing Shen. 2025-09-15. JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference. https://arxiv.org/abs/2509.12104

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