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<p>In recent years, the field of Legal Judgment Prediction (LJP) has advanced substantially, driven by machine learning, deep learning, and large language models. This paper investigates the technical challenges and performance disparities in LJP: while current models excel in charge prediction and legal article recommendation, their capabilities in multi-label prediction, sentencing prediction, and generative tasks require further enhancement. A notable gap persists between laboratory research and real-world judicial needs, encompassing issues such as data quality limitations, inadequate model interpretability, algorithmic bias, and ethical risks. To bridge this gap, we propose a dual framework of “technology optimization–institutional adaptation.”&#xa0;On the technical side, our approach emphasizes enhanced data governance, bias detection, privacy protection, and improved interpretability, while on the institutional side, it advocates for ethical oversight and the integration of AI systems within existing judicial processes. Additionally, we underscore the importance of demand-driven algorithmic improvements include resource-aware model compression, case complexity classification, and robust evaluation protocols to achieve sustainable deployment. Our analysis suggests that only through systematic collaboration among researchers, legal experts, and policymakers can LJP realize both technical innovation and judicial credibility, thereby fostering fairness, efficiency, and trust in legal decision-making.</p>

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Bridging the divide: technical research and application on legal judgment prediction

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Chuyue Zhang,
  • Yuchen Meng

摘要

In recent years, the field of Legal Judgment Prediction (LJP) has advanced substantially, driven by machine learning, deep learning, and large language models. This paper investigates the technical challenges and performance disparities in LJP: while current models excel in charge prediction and legal article recommendation, their capabilities in multi-label prediction, sentencing prediction, and generative tasks require further enhancement. A notable gap persists between laboratory research and real-world judicial needs, encompassing issues such as data quality limitations, inadequate model interpretability, algorithmic bias, and ethical risks. To bridge this gap, we propose a dual framework of “technology optimization–institutional adaptation.” On the technical side, our approach emphasizes enhanced data governance, bias detection, privacy protection, and improved interpretability, while on the institutional side, it advocates for ethical oversight and the integration of AI systems within existing judicial processes. Additionally, we underscore the importance of demand-driven algorithmic improvements include resource-aware model compression, case complexity classification, and robust evaluation protocols to achieve sustainable deployment. Our analysis suggests that only through systematic collaboration among researchers, legal experts, and policymakers can LJP realize both technical innovation and judicial credibility, thereby fostering fairness, efficiency, and trust in legal decision-making.