Explaining Relevance

Abstract

It is currently possible to build computational models of single-case legal evidential reasoning, and many software packages are available on the market that are suitable for the task.¹ It is true that every legal case is different, but we can reasonably hope to find general abstract structures, “consolidative models,”² that can be applied to different cases whenever the same kind of evidence is dealt with.

This Article is the development of some of the ideas I presented in a somewhat different form at the Second World Conference on New Trends in Criminal Investigation and Evidence in Amsterdam, in December 1999, and at the Artificial Intelligence and Judicial Proof Symposium, hosted by the Benjamin N. Cardozo School of Law and The Jacob Burns Institute for Advanced Legal Studies in New York, in April 2000. The discussions held at these conferences have so greatly contributed to this final version that I have to thank all the participants, and in particular, David Schum, for the obvious debt I owe to his work, and Peter Tillers for the fine job performed in organizing the conferences.