research-article

Machine learning for explaining and ranking the most influential matters of law

Authors:

Max R. S. Marques,

Tommaso Bianco,

Maxime Roodnejad,

Claude BerrouAuthors Info & Claims

ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

Pages 239 - 243

https://doi.org/10.1145/3322640.3326734

Published: 17 June 2019 Publication History

Abstract

In this work, we propose a novel method in order to rank the most relevant legal principle citations in law-cases to support a certain motion. The first score relies on feature importance metrics, where each law article is a feature supplied to a classifier for the decision outcome. The second score is based on word embeddings text similarity. As a result, our method outperforms the baseline techniques based on feature importance selection and Information Retrieval methods in the ranking evaluation relevance criteria.

References

[1]

Michałt Araszkiewicz. 2013. Time, Trust and Normative Change. On Certain Sources of Complexity in Judicial Decision-Making. In International Workshop on AI Approaches to the Complexity of Legal Systems. Springer, 100--114.

Digital Library

[2]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with sub word information. Transactions of the Association for Computational Linguistics 5 (2017), 135--146.

[3]

Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32.

Digital Library

[4]

Ilias Chalkidis and Dimitrios Kampas. 2018. Deep learning in law: early adaptation and legal word embeddings trained on large corpora. Artificial Intelligence and Law (dec 2018).

Digital Library

[5]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 785--794.

Digital Library

[6]

Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A survey of methods for explaining black box models. ACM Computing Surveys (CSUR) 51, 5 (2018), 93.

Digital Library

[7]

Marios Koniaris, Ioannis Anagnostopoulos, and Yannis Vassiliou. 2017. Network Analysis in the Legal Domain: A complex model for European Union legal sources. Journal of Complex Networks 6, 2 (2017), 243--268.

[8]

Miron B Kursa, Witold R Rudnicki, et al. 2010. Feature Selection with the Boruta Package. Journal of Statistical Software 36, i11 (2010).

[9]

Tian-Yu Liu. 2009. Easyensemble and feature selection for imbalance data sets. In Bioinformatics, Systems Biology and Intelligent Computing, 2009. IJCBS'09. International Joint Conference on. IEEE, 517--520.

Digital Library

[10]

Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 4765--4774.

Digital Library

[11]

Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, and Dongyan Zhao. 2017. Learning to Predict Charges for Criminal Cases with Legal Basis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2727--2736.

[12]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.

Digital Library

[13]

Yannis Panagis, Urska Sadl, and Fabien Tarissan. 2017. Giving every case its (legal) due The contribution of citation networks and text similarity techniques to legal studies of European Union law. In 30th International Conference on Legal Knowledge and Information Systems (JURIX'17), Vol. 302. IOS Press, 59--68.

[14]

Marc Van Opijnen and Cristiana Santos. 2017. On the concept of relevance in legal information retrieval. Artificial Intelligence and Law 25, 1 (2017), 65--87.

Digital Library

[15]

Jorge R Vergara and Pablo A Estévez. 2014. A review of feature selection methods based on mutual information. Neural computing and applications 24, 1 (2014), 175--186.

[16]

Yining Wang, Liwei Wang, Yuanzhi Li, Di He, and Tie-Yan Liu. 2013. A theoretical analysis of NDCG type ranking measures. In Conference on Learning Theory. 25--54.

[17]

Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, and Jianfeng Xu. 2018. CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction. CoRR abs/1807.02478 (2018).

Cited By

Alvares AVarghese LJesus TShaikh S(2023)Women’s Rights in India2023 International Conference on Advanced Computing Technologies and Applications (ICACTA)10.1109/ICACTA58201.2023.10393361(1-6)Online publication date: 6-Oct-2023
https://doi.org/10.1109/ICACTA58201.2023.10393361
Moussaoui TChakir LBoumhidi J(2023)Preserving Privacy in Arabic Judgments: AI-Powered Anonymization for Enhanced Legal Data PrivacyIEEE Access10.1109/ACCESS.2023.332428811(117851-117864)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3324288
Shang X(2022)A Computational Intelligence Model for Legal Prediction and Decision SupportComputational Intelligence and Neuroscience10.1155/2022/57951892022(1-8)Online publication date: 24-Jun-2022
https://doi.org/10.1155/2022/5795189
Show More Cited By

Index Terms

Machine learning for explaining and ranking the most influential matters of law
1. Applied computing
  1. Law, social and behavioral sciences
    1. Law

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cover image ACM Conferences

ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

June 2019

312 pages

ISBN:9781450367547

DOI:10.1145/3322640

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Univ. of Montreal: University of Montreal
AAAI
IAAIL: Intl Asso for Artifical Intel & Law

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2019

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ICAIL '19

Sponsor:

SIGAI

ICAIL '19: Seventeenth International Conference on Artificial Intelligence and Law

June 17 - 21, 2019

QC, Montreal, Canada

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Overall Acceptance Rate 69 of 169 submissions, 41%

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Cited By

Alvares AVarghese LJesus TShaikh S(2023)Women’s Rights in India2023 International Conference on Advanced Computing Technologies and Applications (ICACTA)10.1109/ICACTA58201.2023.10393361(1-6)Online publication date: 6-Oct-2023
https://doi.org/10.1109/ICACTA58201.2023.10393361
Moussaoui TChakir LBoumhidi J(2023)Preserving Privacy in Arabic Judgments: AI-Powered Anonymization for Enhanced Legal Data PrivacyIEEE Access10.1109/ACCESS.2023.332428811(117851-117864)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3324288
Shang X(2022)A Computational Intelligence Model for Legal Prediction and Decision SupportComputational Intelligence and Neuroscience10.1155/2022/57951892022(1-8)Online publication date: 24-Jun-2022
https://doi.org/10.1155/2022/5795189
Hei MLian XZhang XChen DFan J(2021)Penalty Prediction in Discipline Reinforcement Based on Multi-Task Learning2021 7th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA53151.2021.9619644(510-515)Online publication date: 29-Oct-2021
https://doi.org/10.1109/BigDIA53151.2021.9619644
Allard TBéziaud LGambs S(2021)Publication of Court Records: Circumventing the Privacy-Transparency Trade-OffAI Approaches to the Complexity of Legal Systems XI-XII10.1007/978-3-030-89811-3_21(298-312)Online publication date: 27-Nov-2021
https://doi.org/10.1007/978-3-030-89811-3_21

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