A comparative study of uncertainty methods for legal reasoning

Abstract

This paper is based on the premise that legal reasoning involves an evaluation of facts, principles, and legal precedent that are inexact, and uncertainty-based methods represent a useful approach for modeling this type of reasoning. By applying three different uncertainty-based methods to the same legal reasoning problem, a comparative study can be constructed. The application involves modeling legal reasoning for the assessment of potential liability due to defective product design. The three methods used for this study include: a Bayesian belief network, a fuzzy logic system, and an artificial neural network. A common knowledge base is used to implement the three solutions and provide an unbiased framework for evaluation. The problem framework and the construction of the common knowledgebase are described. The theoretical background for Bayesian belief networks, fuzzy logic inference, and multilayer perceptron with backpropagation are discussed. The design, implementation, and results with each of these systems are provided. The fuzzy logic system outperformed the other systems by reproducing the opinion of a skilled attorney in 99 of 100 cases, but the fuzzy logic system required more effort to construct the rulebase. The neural network method also reproduced the expert's opinions very well, but required less effort to develop.