Near Knowledge: Inductive Learning Systems in Law

37 Pages Posted: 15 Sep 2000

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.

JEL Classification: K49

Suggested Citation

Hunter, Dan, Near Knowledge: Inductive Learning Systems in Law. Virginia Journal of Law and Technology. Available at SSRN: https://ssrn.com/abstract=239742 or http://dx.doi.org/10.2139/ssrn.239742

Dan Hunter (Contact Author)

Swinburne Law School ( email )

Cnr Wakefield and William Streets, Hawthorn Victor
3122 Victoria, Victoria 3122
Australia

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