Law, learning and representation

  • Authors:

  • Kevin D. Ashley;Edwina L. Rissland

  • Affiliations:

  • Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA;Department of Computer Science, University of Massachusetts, Amherst, MA

  • Venue:
  • Artificial Intelligence - Special issue on AI and law
  • Year:
  • 2003

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Abstract

In machine learning terms, reasoning in legal cases can be compared to a lazy learning approach in which courts defer deciding how to generalize beyond the prior cases until the facts of a new case are observed. The HYPO family of systems implements a "lazy" approach since they defer making arguments how to decide a problem until the programs have positioned a new problem with respect to similar past cases. In a kind of "reflective adjustment", they fit the new problem into a patchwork of past case decisions, comparing cases in order to reason about the legal significance of the relevant similarities and differences. Empirical evidence from diverse experiments shows that for purposes of teaching legal argumentation and performing legal information retrieval, HYPO-style systems' lazy learning approach and implementation of aspects of reflective adjustment can be very effective.