Article

Visualizing association rules for feedback within the legal system

Authors:

Andrew StranieriAuthors Info & Claims

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

Pages 214 - 223

https://doi.org/10.1145/1047788.1047835

Published: 24 June 2003 Publication History

Abstract

Knowledge discovery from databases (KDD) exercises in law have typically attempted to derive knowledge about decision making processes in the legal domain automatically from datasets. This is made difficult in that real data that represents aspects of a decision process in law is commonly stored as text and rarely stored in structured databases. The central claim advanced here is that KDD processes can be usefully applied to existing datasets of client and demographic data in order to provide feedback for the effective operation of organizations within the legal system. However, the cost of data mining suites and the scarcity of specialized personnel for these tools mitigates against their use. In this study data mining with Association Rules (AR) has been performed on a data-set of over 380,000 records from a legal aid agency. Methods to visualise patterns in order to suggest and test plausible hypotheses from the data have been developed. The tool, called WebAssociate is entirely web based. Domain experts using the tool report favorable responses.

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Cited By

Nissan ENissan E(2011)Accounting for Social, Spatial, and Textual InterconnectionsComputer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation10.1007/978-90-481-8990-8_6(483-765)Online publication date: 24-Oct-2011
https://doi.org/10.1007/978-90-481-8990-8_6
Goh DAng R(2007)An introduction to association rule mining: An application in counseling and help-seeking behavior of adolescentsBehavior Research Methods10.3758/BF0319315639:2(259-266)Online publication date: May-2007
https://doi.org/10.3758/BF03193156
Saunders GIvkovic SGhosh RYearwood J(2005)Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signallingProceedings of the 2005 Australasian Ontology Workshop - Volume 5810.5555/1151936.1151948(93-98)Online publication date: 1-Nov-2005
https://dl.acm.org/doi/10.5555/1151936.1151948
Show More Cited By

Index Terms

Visualizing association rules for feedback within the legal system
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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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

Nissan ENissan E(2011)Accounting for Social, Spatial, and Textual InterconnectionsComputer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation10.1007/978-90-481-8990-8_6(483-765)Online publication date: 24-Oct-2011
https://doi.org/10.1007/978-90-481-8990-8_6
Goh DAng R(2007)An introduction to association rule mining: An application in counseling and help-seeking behavior of adolescentsBehavior Research Methods10.3758/BF0319315639:2(259-266)Online publication date: May-2007
https://doi.org/10.3758/BF03193156
Saunders GIvkovic SGhosh RYearwood J(2005)Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signallingProceedings of the 2005 Australasian Ontology Workshop - Volume 5810.5555/1151936.1151948(93-98)Online publication date: 1-Nov-2005
https://dl.acm.org/doi/10.5555/1151936.1151948
Zeleznikow J(2004)Building Intelligent Legal Decision Support Systems: Past Practice and Future ChallengesApplied Intelligent Systems10.1007/978-3-540-39972-8_7(201-254)Online publication date: 2004
https://doi.org/10.1007/978-3-540-39972-8_7

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