research-article

Using Factors to Predict and Analyze Landlord-Tenant Decisions to Increase Access to Justice

Authors:

Hannes Westermann,

Vern R. Walker,

Kevin D. Ashley,

Karim BenyekhlefAuthors Info & Claims

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

Pages 133 - 142

https://doi.org/10.1145/3322640.3326732

Published: 17 June 2019 Publication History

Abstract

This paper reports results from the JusticeBot Project, in which we analyzed two datasets drawn from 1 million written decisions from the Régie du logement du Québec. Using an empirical methodology, we identified 44 factors that occur in disputes where the tenant seeks a remedy due to problems with the rented apartment, such as the existence of bedbugs, high noise levels or problems with insulation. In the first dataset, we used these factors to tag 149 cases. We found a correlation between how many factors are found in a case and how likely the judge is to award rent reduction to a tenant; the amount of reduction was also higher in cases with more factors. For the second dataset (39 cases with bedbugs, drawn from the first dataset), we developed in-depth factors and used them to tag the cases. We found a number of plausible correlations, such as the average damage award being higher in cases with infestations of high intensity. Finally, in predicting the decision of the judge using the factors present in a case, the results were similar to the baselines or slightly above. We discuss the possible reasons for this, and why the approach shows promise in providing useful information to lay people and lawyers.

References

[1]

Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro and Vasileios Lampos. 2016. Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective. PeerJ Computer Science 2, e93 (October 2016), 19 pages.

[2]

Vincent Aleven. 2003. Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment. Artificial Intelligence 150, 1-2 (November 2003), 183--237.

Digital Library

[3]

Kevin D. Ashley. 2017. Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press, Cambridge, United Kingdom.

[4]

Kevin D. Ashley and Stefanie Brüninghaus. 2009. Automatically classifying case texts and predicting outcomes. Artificial Intelligence and Law 17, 2 (2009), 125--165.

Digital Library

[5]

Katie Atkinson and Trevor Bench-Capon. 2018. Lessons from implementing factors with magnitude. In Legal Knowledge and Information Systems: JURIX 2018: The Thirty-first Annual Conference, ed. Monica Palmirani (Amsterdam: IOS Press, 2018), 11--20.

[6]

Or Biran and Courtenay Cotton. 2017. Explanation and justification in machine learning: A survey (IJCAI 2017). Retrieved January 27, 2019 from http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/

[7]

Alison Chorley and Trevor Bench-Capon. 2005. AGATHA: Using heuristic search to automate the construction of case law theories. Artificial Intelligence and Law 13, 1 (March 2005), 9--51.

Digital Library

[8]

Mohammad H. Falakmasir and Kevin D. Ashley. 2017. Utilizing Vector Space Models for Identifying Legal Factors from Text. In Legal Knowledge and Information Systems: JURIX 2017: The Thirtieth Annual Conference, eds. Adam Wyner and Giovanni Casini (Amsterdam: IOS Press, 2017), 183--192.

[9]

Matthias Grabmair. 2017. Predicting trade secret case outcomes using argument schemes and learned quantitative value effect tradeoffs. In Proceedings of the 16th edition of the International Conference on Artificial Intelligence and Law (ICAIL '17). ACM, New York, NY, USA, 89--98.

Digital Library

[10]

Octavia-Maria Sulea, Marcos Zampieri, Mihaela Vela and Josef van Genabith. 2017. Predicting the law area and decisions of french supreme court cases. arXiv:1701.170801681. Retrieved from https://arxiv.org/abs/1708.01681.

[11]

Vern R. Walker. 2007. A Default-Logic Paradigm for Legal Fact-Finding. Jurimetrics 47, 2 (2007), 193--243. Retrieved January 28, 2019 from http://www.jstor.org/stable/29762966.

[12]

Vern R. Walker, Nathaniel Carie, Courtney C. DeWitt, and Eric Lesh. 2011. A framework for the extraction and modeling of fact-finding reasoning from legal decisions: lessons from the Vaccine/Injury Project Corpus. Artificial Intelligence and Law 19, 4 (August 2011), 291--331.

Digital Library

[13]

Vern R. Walker, Bernadette C. Lopez, Matthew T. Rutchik, and Julie L. Agris. 2015. Representing the Logic of Statutory Rules in the United States. In Legisprudence Library (Studies on the Theory and Practice of Legislation), vol 2: Logic in the Theory and Practice of Lawmaking, eds. Michałt Araszkiewicz and Krzysztof Płteszka (Cham: Springer, 2015), 357--381.

[14]

Vern R. Walker, Ji Hae Han, Xiang Ni, and Kaneyasu Yoseda. 2017. Semantic types for computational legal reasoning: propositional connectives and sentence roles in the veterans' claims dataset. In Proceedings of the 16th edition of the International Conference on Artificial Intelligence and Law (ICAIL '17). ACM, New York, NY, USA, 217--226.

Digital Library

[15]

Adam Wyner and Wim Peters. 2010. Towards annotating and extracting textual legal case factors. In Semantic Processing of Legal Texts (SPLeT-2012), 36--45. Retrieved January 28, 2019 from http://www.lrec-conf.org/proceedings/lrec2010/workshops/W23.pdf.

[16]

Adam Wyner and Wim Peters. 2012. Semantic Annotations for Legal Text Processing using GATE Teamware. In Semantic Processing of Legal Texts (SPLeT-2012), 34--36. Retrieved January 28, 2019 from http://www.lrec-conf.org/proceedings/lrec2012/workshops/27.LREC%202012%20Workshop%20Proceedings%20SPLeT.pdf.

[17]

Lisa Webley. 'Qualitative Approaches to Empirical Legal Research' in The Oxford Handbook of Empirical Legal Research, eds. Peter Cane and Herbert Kritzer (Oxford: Oxford University Press, 2010), 926--950.

[18]

Jaromír Šavelka and Kevin D. Ashley. 2018. Segmenting U.S. Court Decisions into Functional and Issue Specific Parts. In Legal Knowledge and Information Systems: JURIX 2018: The Thirty-first Annual Conference, ed. Monica Palmirani (Amsterdam: IOS Press, 2018), 111--120.

Cited By

Gray MSavelka JOliver WAshley K(2024)Empirical legal analysis simplified: reducing complexity through automatic identification and evaluation of legally relevant factorsPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences10.1098/rsta.2023.0155382:2270Online publication date: 26-Feb-2024
https://doi.org/10.1098/rsta.2023.0155
Drápal JWestermann HSavelka J(2023)Using Large Language Models to Support Thematic Analysis in Empirical Legal StudiesSSRN Electronic Journal10.2139/ssrn.4617116Online publication date: 2023
https://doi.org/10.2139/ssrn.4617116
Westermann HBenyekhlef KAndrade FGrabmair M(2023)JusticeBotProceedings of the Nineteenth International Conference on Artificial Intelligence and Law10.1145/3594536.3595166(351-360)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3594536.3595166
Show More Cited By

Index Terms

Using Factors to Predict and Analyze Landlord-Tenant Decisions to Increase Access to Justice
1. Applied computing
  1. Law, social and behavioral sciences
    1. Law
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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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 the author(s) 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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Published: 17 June 2019

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ICAIL '19

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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

Gray MSavelka JOliver WAshley K(2024)Empirical legal analysis simplified: reducing complexity through automatic identification and evaluation of legally relevant factorsPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences10.1098/rsta.2023.0155382:2270Online publication date: 26-Feb-2024
https://doi.org/10.1098/rsta.2023.0155
Drápal JWestermann HSavelka J(2023)Using Large Language Models to Support Thematic Analysis in Empirical Legal StudiesSSRN Electronic Journal10.2139/ssrn.4617116Online publication date: 2023
https://doi.org/10.2139/ssrn.4617116
Westermann HBenyekhlef KAndrade FGrabmair M(2023)JusticeBotProceedings of the Nineteenth International Conference on Artificial Intelligence and Law10.1145/3594536.3595166(351-360)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3594536.3595166
Pawar SAli BPalshikar GSingh RSingh DAndrade FGrabmair M(2023)Extraction and Classification of Statute Facets using Few-shot LearningProceedings of the Nineteenth International Conference on Artificial Intelligence and Law10.1145/3594536.3595134(197-206)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3594536.3595134
Hartline JLinna DShan LTang AWeitzner DFeigenbaum JYoo C(2022)Algorithmic Learning Foundations for Common LawProceedings of the 2022 Symposium on Computer Science and Law10.1145/3511265.3550438(109-117)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3511265.3550438
Rodríguez M(2021)Ciberseguridad en la justicia digital: recomendaciones para el caso colombianoRevista UIS Ingenierías10.18273/revuin.v20n3-202100220:3Online publication date: 10-May-2021
https://doi.org/10.18273/revuin.v20n3-2021002
Garneau NGaumond ELamontagne LDéziel PMaranhão JWyner A(2021)Plum2TextProceedings of the Eighteenth International Conference on Artificial Intelligence and Law10.1145/3462757.3466148(200-204)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3462757.3466148
Salaün OLanglais PBenyekhlef KMaranhão JWyner A(2021)Labels distribution matters in performance achieved in legal judgment prediction tasksProceedings of the Eighteenth International Conference on Artificial Intelligence and Law10.1145/3462757.3466144(268-269)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3462757.3466144
Lou ASalaün OWestermann HKosseim L(2021)Extracting Facts from Case Rulings Through Paragraph Segmentation of Judicial DecisionsNatural Language Processing and Information Systems10.1007/978-3-030-80599-9_17(187-198)Online publication date: 20-Jun-2021
https://doi.org/10.1007/978-3-030-80599-9_17
Branting LPfeifer CBrown BFerro LAberdeen JWeiss BPfaff MLiao B(2020)Scalable and explainable legal predictionArtificial Intelligence and Law10.1007/s10506-020-09273-1Online publication date: 24-Jun-2020
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