Interpretable Legal Judgment Reasoning Framework: Improving Forecasts of Case Outcomes with Multi-Source Knowledge

Sudhir Kumar Sharma

Interpretable Legal Judgment Reasoning Framework: Improving Forecasts of Case Outcomes with Multi-Source Knowledge

Authors: Sudhir Kumar Sharma

Published: 2025-02-21 13:03:33.849

Abstract

This paper presents an interpretable legal judgment reasoning framework that aims to improve both the accuracy and interpretability of legal judgment predictions. The framework covers five key areas: limitations of existing methods, the role of factual logic in judgments, integration of external legal knowledge, handling of long-tail and ambiguous cases, and overall interpretability. The methodology adopted is qualitative, involving experimental data analysis and user feedback. This framework combines factually based logic with legal knowledge using a chain prompt reasoning module and a contrastive knowledge fusing module. Therefore, the result shows considerable improvement in terms of prediction accuracy and interpretability. These advances will fill important gaps in the existing literature on LJP research and represent a dynamic, transparent approach to judicial decision-making.

Keywords

Legal judgment prediction, factual logic, legal knowledge integration, long-tail cases

References

Hu, Z., Wei, Y., & Xu, J. (2023). Interpretable Legal AI: Methods and Challenges in Judgment Prediction. Journal of Artificial Intelligence and Law, 31(2), 123-145
Miller, T. (2019). Explanation in Artificial Intelligence: Insights from the Social Sciences. Artificial Intelligence, 267, 1-38
Raghavan, S., & Barzilay, R. (2021). Incorporating Legal Knowledge in Case Predictions Using NLP. Proceedings of the 59th Annual Meeting of the ACL, 1340-1350
Zhong, H., Zhang, T., & Sun, M. (2020). Legal Judgment Prediction via Deep Learning: Addressing Interpretability and Accuracy. Proceedings of EMNLP, 2057-2066
Wang, Y., & Li, F. (2022). Dynamic Knowledge Integration for Legal AI. IEEE Transactions on Knowledge and Data Engineering, 34(6), 1-12
Bench-Capon, T., & Sartor, G. (2019). A Model of Legal Reasoning with Cases Incorporating Theories and Values. Artificial Intelligence and Law, 27(1), 29-68
Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., & Lampos, V. (2016). Predicting Judicial Decisions of the European Court of Human Rights: A Natural Language Processing Perspective. PeerJ Computer Science, 2, e93
Sun, C., Feng, X., & Wan, X. (2021). Legal Judgment Prediction with Topological Knowledge and Contextual Representations. Proceedings of NAACL, 1642-1651
Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., & Androutsopoulos, I. (2019). Neural Legal Judgment Prediction in English. Proceedings of ACL, 4317-4323
Ashley, K. D. (2017). Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press
Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020). How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence. Proceedings of ACL, 5218-5230
Katz, D. M., Bommarito, M. J., & Blackman, J. (2017). A General Approach for Predicting the Behavior of the Supreme Court of the United States. PLoS ONE, 12(4), e0174698
Jiang, M., Li, S., & Xu, W. (2022). Incorporating Domain Knowledge into Legal Decision Prediction with Neural Networks. Neural Computing and Applications, 34(4), 1203-1215
Liu, Z., Wang, T., & He, L. (2021). Legal Knowledge Graphs for Judgment Prediction. Knowledge-Based Systems, 228, 107258

Download PDF

How to Cite

Sudhir Kumar Sharma, (2025-02-21 13:03:33.849). Interpretable Legal Judgment Reasoning Framework: Improving Forecasts of Case Outcomes with Multi-Source Knowledge. Abhi International Journal of Information Processing Management, Volume oPMI31nYkkzgNQohcE9Z, Issue 1.

ISSN: XXXX-XXXX

Abhi International Journal of
Information Processing Management ( AIJIPM )