Article

Predicting outcomes of case based legal arguments

Authors:

Stefanie Bruninghaus,

Kevin D. AshleyAuthors Info & Claims

ICAIL '03: Proceedings of the 9th international conference on Artificial intelligence and law

Pages 233 - 242

https://doi.org/10.1145/1047788.1047838

Published: 24 June 2003 Publication History

Abstract

In this paper, we introduce IBP, an algorithm that combines reasoning with an abstract domain model and case-based reasoning techniques to predict the outcome of case-based legal arguments. Unlike the predictions generated by statistical or machine-learning techniques, IBP's predictions are accompanied by explanations.We describe an empirical evaluation of IBP, in which we compare our algorithm to prediction based on Hypo's and CATO's relevance criteria, and to a number of widely used machine learning algorithms. IBP reaches higher accuracy than all competitors, and hypothesis testing shows that the observed differences are statistically significant. An ablation study indicates that both sources of knowledge in IBP contribute to the accuracy of its predictions.

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Cited By

Wen YTi P(2024)A Study of Legal Judgment Prediction Based on Deep Learning Multi-Fusion Models—Data from ChinaSage Open10.1177/2158244024125768214:3Online publication date: 9-Sep-2024
https://doi.org/10.1177/21582440241257682
Rigoni A(2024)Toward representing interpretation in factor-based models of precedentArtificial Intelligence and Law10.1007/s10506-023-09384-5Online publication date: 12-Jan-2024
https://doi.org/10.1007/s10506-023-09384-5
Sachan SDezem VFickett D(2024)Blockchain for Ethical and Transparent Generative AI Utilization by Banking and Finance LawyersExplainable Artificial Intelligence10.1007/978-3-031-63800-8_16(319-333)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-3-031-63800-8_16
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Predicting outcomes of case based legal arguments
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Published In

cover image ACM Conferences

ICAIL '03: Proceedings of the 9th international conference on Artificial intelligence and law

June 2003

304 pages

ISBN:1581137478

DOI:10.1145/1047788

Conference Chair:
John Zeleznikow
The Edinburgh Law School, University of Edinburgh, Scotland, UK
,
Program Chair:
Giovanni Sartor
CIRSFID, University of Bologna, Italy

Copyright © 2003 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]

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence
West Group, Thomson Legal & Regulatory
The International Association for Artificial Intelligence and Law
The Joseph Bell Centre for Forensic Statistics and Legal Reasoning
The University of Edinburgh School of Law

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2003

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Cited By

Wen YTi P(2024)A Study of Legal Judgment Prediction Based on Deep Learning Multi-Fusion Models—Data from ChinaSage Open10.1177/2158244024125768214:3Online publication date: 9-Sep-2024
https://doi.org/10.1177/21582440241257682
Rigoni A(2024)Toward representing interpretation in factor-based models of precedentArtificial Intelligence and Law10.1007/s10506-023-09384-5Online publication date: 12-Jan-2024
https://doi.org/10.1007/s10506-023-09384-5
Sachan SDezem VFickett D(2024)Blockchain for Ethical and Transparent Generative AI Utilization by Banking and Finance LawyersExplainable Artificial Intelligence10.1007/978-3-031-63800-8_16(319-333)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-3-031-63800-8_16
Mumford JAtkinson KBench-Capon TAndrade FGrabmair M(2023)Combining a Legal Knowledge Model with Machine Learning for Reasoning with Legal CasesProceedings of the Nineteenth International Conference on Artificial Intelligence and Law10.1145/3594536.3595158(167-176)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3594536.3595158
Bench-Capon TAndrade FGrabmair M(2023)Whatever Happened to Hypotheticals?Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law10.1145/3594536.3595138(387-391)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3594536.3595138
Atkinson KBench-Capon TAndrade FGrabmair M(2023)ANGELIC IIProceedings of the Nineteenth International Conference on Artificial Intelligence and Law10.1145/3594536.3595137(12-21)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3594536.3595137
Collenette JAtkinson KBench-Capon T(2023)Explainable AI tools for legal reasoning about cases: A study on the European Court of Human RightsArtificial Intelligence10.1016/j.artint.2023.103861317(103861)Online publication date: Apr-2023
https://doi.org/10.1016/j.artint.2023.103861
Trancoso IMamede NMartins BPinto HRibeiro R(2023)The Impact of Language Technologies in the Legal DomainMultidisciplinary Perspectives on Artificial Intelligence and the Law10.1007/978-3-031-41264-6_2(25-46)Online publication date: 27-Dec-2023
https://doi.org/10.1007/978-3-031-41264-6_2
Zufall FKimura RPeng L(2022)Towards a simple mathematical model for the legal concept of balancing of interestsArtificial Intelligence and Law10.1007/s10506-022-09338-331:4(807-827)Online publication date: 8-Nov-2022
https://doi.org/10.1007/s10506-022-09338-3
Governatori GBench-Capon TVerheij BAraszkiewicz MFrancesconi EGrabmair M(2022)Thirty years of Artificial Intelligence and Law: the first decadeArtificial Intelligence and Law10.1007/s10506-022-09329-430:4(481-519)Online publication date: 6-Sep-2022
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