Near knowledge : inductive learning systems in law

Hunter, Dan (2000) Near knowledge : inductive learning systems in law. Virginia Journal of Law and Technology, 5(9), pp. 1522-1687.

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Abstract

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.

Impact and interest:

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ID Code: 71110
Item Type: Journal Article
Refereed: No
Additional URLs:
ISSN: 1522-1687
Divisions: Current > QUT Faculties and Divisions > Faculty of Law
Current > Schools > School of Law
Copyright Owner: Copyright 2000 University of Virginia * School of Law
Deposited On: 07 May 2014 01:21
Last Modified: 07 May 2014 01:21

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