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.
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:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|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|
Repository Staff Only: item control page