Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models
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 an...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02009.pdf |
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| _version_ | 1825206572130762752 |
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| author | Wu Yifan |
| author_facet | Wu Yifan |
| author_sort | Wu Yifan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7f47e88e98d742d4ba02c4658daec784 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-7f47e88e98d742d4ba02c4658daec7842025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700200910.1051/itmconf/20257002009itmconf_dai2024_02009Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction ModelsWu Yifan0School of Cyber Security and Information Law, Chongqing University of Posts and TelecommunicationsEnsuring 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.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02009.pdf |
| spellingShingle | Wu Yifan Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models ITM Web of Conferences |
| title | Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models |
| title_full | Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models |
| title_fullStr | Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models |
| title_full_unstemmed | Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models |
| title_short | Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models |
| title_sort | reducing judicial inconsistency through ai a review of legal judgement prediction models |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02009.pdf |
| work_keys_str_mv | AT wuyifan reducingjudicialinconsistencythroughaiareviewoflegaljudgementpredictionmodels |