Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 02009 | |
Number of page(s) | 8 | |
Section | Machine Learning in Healthcare and Finance | |
DOI | https://doi.org/10.1051/itmconf/20257002009 | |
Published online | 23 January 2025 |
Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models
School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, China
Corresponding author: 2022212705@stu.cqupt.edu.cn
Ensuring equitable sentencing is a fundamental objective of the judicial system. However, disparities in law enforcement standards, policies, and personnel competence across regions can lead to divergent sentencing outcomes for similar cases. This inconsistency undermines the integrity of justice and diminishes public confidence. With the development of AI technology, especially in the field of NLP, more and more researchers are focusing on the role that AI can play in legal judgements, and the LJP model has been developed. The LJP model is widely expected to help reduce the judicial inconsistency that currently exists, and better help to maintain the fairness and justice of the law. This paper summarizes the latest developments in the field of LJP, introduces and compares some of the current representative works, including the advantages and disadvantages of current technology. After that, it discusses possible future research directions and considers the significance of the development of this field.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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