- Title
- Insights from jurisprudence for machine learning in law
- Creator
- Stranieri, Andrew; Zeleznikow, John
- Date
- 2012
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/63428
- Identifier
- vital:5888
- Identifier
-
https://doi.org/10.4018/978-1-4666-1833-6.ch006
- Identifier
- ISBN:978-146661833-6
- 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 reasoning. 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.
- Publisher
- Hershey IGI Global
- Relation
- Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 85-98
- Rights
- © 2012 by IGI Global. All rights reserved.
- Rights
- This metadata is freely available under a CCO license
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