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Insights from Jurisprudence for Machine Learning in Law

Stranieri, Andrew and Zeleznikow, John (2012) Insights from Jurisprudence for Machine Learning in Law. In: Machine Learing Algorithms for Problem Solving in Computational Applications : Intelligent Techniques. Kulkarni, Siddhivinayak, ed. IGI Global Information Science, Hershey, Pennsylvania, pp. 85-98.

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Abstract

The central theme of this chapter is that the application of machine learning to data in the legal domain involves considerations that derive from jurisprudential assumptions about the nature of legal reason - ing. Jurisprudence provides a unique resource for machine learning in that, for over one hundred years, significant thinkers have advanced concepts including open texture and discretion. These concepts inform and guide applications of machine learning to law

Item Type: Book Section
ISBN: 9781466618336 (hardback), 9781466618343 (online)
Uncontrolled Keywords: ResPubID26355, algorithms, Split-Up, Family Court of Australia, split property assets, court case predictions, case law
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
FOR Classification > 1801 Law
Faculty/School/Research Centre/Department > Graduate School of Business
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Depositing User: VUIR
Date Deposited: 31 Jul 2013 05:33
Last Modified: 31 Jul 2013 05:33
URI: http://vuir.vu.edu.au/id/eprint/10702
DOI: 10.4018/978-1-4666-1833-6.ch006
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