ISSN: XXXX-XXXX
Abhi International Journal of
Information Processing Management ( AIJIPM )
Login/Register
For Reference
About Journal Aim and Scope Editorial Board Publication Ethics Indexing And Abstracting Peer Review Process About Journal Aim and Scope Editorial Board Publication Ethics Indexing And Abstracting Peer Review Process

Enhancing Legal Judgment Prediction: An Interpretable Framework with Multi-Source Knowledge

Authors: Pramod Kumar Arya

Published: 2025-01-07 17:49:18.534

Download PDF

Abstract

This paper studies the challenges and progressions of Legal Judgment Prediction, in particular on the grounds of improving efficiency, accuracy, and fairness in judicial systems. The study examines five sub-research questions: the limitation of existing LJP methods, the role of factual logic in judgment reasoning, the integration of external legal knowledge, the effectiveness of a chain prompt reasoning module, and the impact of contrastive knowledge fusion on long-tail cases. A qualitative research methodology is followed to design and validate an interpretable framework for LJP, featuring a chain prompt reasoning module to strengthen factual logic and a contrastive knowledge fusing module to incorporate external legal knowledge. Results indicate notable improvements in the prediction accuracy, interpretability, and handling of complex long-tail cases. Despite the specific datasets used, the proposed framework is a demonstration of its potential in wider applications and theoretical contributions to legal AI. Future work will be based on diverse data sources and methodologies to generalize and improve these findings.

Keywords

Long-tail cases, Legal knowledge integration, Legal AI advancements, Judicial system efficiency

References

  1. 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.
  2. Narendra Kumar, B. Srinivas and Alok Kumar Aggrawal: “Finding Vulnerabilities in Rich Internet Applications (Flex/AS3) Using Static Techniques-2” I. J. Modern Education and Computer Science, 2012, 1, 33-39.(http://www.mecs-press.org/ DOI: 10.5815/ijmecs.2012.01.05)
  3. Anuj Kumar, Narendra Kumar and Alok Aggrawal: “An Analytical Study for Security and Power Control in MANET” International Journal of Engineering Trends and Technology, Vol 4(2), 105-107, 2013.
  4. Anuj Kumar, Narendra Kumar and Alok Aggrawal: “Balancing Exploration and Exploitation using Search Mining Techniques” in IJETT, 3(2), 158-160, 2012
  5. Anuj Kumar, Shilpi Srivastav, Narendra Kumar and Alok Agarwal “Dynamic Frequency Hopping: A Major Boon towards Performance Improvisation of a GSM Mobile Network” International Journal of Computer Trends and Technology, vol 3(5) pp 677-684, 2012.
  6. Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., & Sun, M. (2020). Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 3343–3353.
  7. Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., & Androutsopoulos, I. (2019). Neural Legal Judgment Prediction in English. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 4317–4323.
  8. Long, Y., Zhong, H., Liu, Z., & Sun, M. (2019). Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network. Proceedings of the 27th International Conference on Computational Linguistics (COLING), 3717–3728.
  9. Sulea, O.-M., Zampieri, M., Malmasi, S., Vela, M., Dinu, L. P., & van Genabith, J. (2017). Exploring the Use of Text Classification Techniques for the Prediction of Judicial Decisions. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2771–2777.
  10. Lin, X., Zhang, J., Shao, Z., & Sun, M. (2021). Explainable Legal Judgment Prediction Using
  11. Case Knowledge and Rule Representation. Artificial Intelligence and Law, 29(2), 231-252. Ashley, K. D. (2017). Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press.
  12. Medvedeva, M., Vols, M., & Wieling, M. (2019). Using Machine Learning to Predict Decisions of the European Court of Human Rights. Artificial Intelligence and Law, 27(2), 237-266.
  13. Hu, Y., Zeng, Q., He, Z., & Yuan, X. (2022). Dynamic Knowledge Integration in Legal AI: Challenges and Prospects. Journal of AI Research in Law, 5(1), 55-70.
  14. Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50-57.
Download PDF

How to Cite

Pramod Kumar Arya, (2025-01-07 17:49:18.534). Enhancing Legal Judgment Prediction: An Interpretable Framework with Multi-Source Knowledge. Abhi International Journal of Information Processing Management, Volume bgjq8zH6kshH0MaeJVQL, Issue 1.

Follow Us
Get In Touch

Vitthal Namdev Nagar, Opp Jain Temple Sanganer Sanganer Bazar Jaipur 302029 Rajasthan (India)

mail@abhijournals.com

© Abhi International Journals. All Rights Reserved.