Predicting outcomes of case based legal arguments

  • Authors:

  • Stefanie Bruninghaus;Kevin D. Ashley

  • Affiliations:

  • University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA

  • Venue:
  • ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
  • Year:
  • 2003

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Abstract

In this paper, we introduce IBP, an algorithm that combines reasoning with an abstract domain model and case-based reasoning techniques to predict the outcome of case-based legal arguments. Unlike the predictions generated by statistical or machine-learning techniques, IBP's predictions are accompanied by explanations.We describe an empirical evaluation of IBP, in which we compare our algorithm to prediction based on Hypo's and CATO's relevance criteria, and to a number of widely used machine learning algorithms. IBP reaches higher accuracy than all competitors, and hypothesis testing shows that the observed differences are statistically significant. An ablation study indicates that both sources of knowledge in IBP contribute to the accuracy of its predictions.