Insights from Jurisprudence for Machine Learning in Law

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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.

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

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/10702
DOI https://doi.org/10.4018/978-1-4666-1833-6.ch006
ISBN 9781466618336 (hardback), 9781466618343 (online)
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1801 Law
Current > Division/Research > Graduate School of Business
Keywords ResPubID26355, algorithms, Split-Up, Family Court of Australia, split property assets, court case predictions, case law
Citations in Scopus 2 - View on Scopus
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