Work on a computer program called SMILE + IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of Factors, a (...) set of classification concepts that capture stereotypical fact patterns that effect the strength of a legal claim, here trade secret misappropriation. Using these classifications, the program can evaluate and explain predictions about a problem’s outcome given a database of previously classified cases. This paper provides an extended example illustrating both functions, prediction by IBP and text classification by SMILE, and reports empirical evaluations of each. While IBP’s results are quite strong, and SMILE’s much weaker, SMILE + IBP still has some success predicting and explaining the outcomes of case scenarios input as texts. It marks the first time to our knowledge that a program can reason automatically about legal case texts. (shrink)

The concept of conductive argument weight was developed by Carl Wellman and later by Trudy Govier. This concept has received renewed attention recently from another informal logician, Robert C. Pinto. Argument weight has also been addressed in recent years by theorists in AI & Law. I argue from a non-technical perspective that some aspects of AI & Law’s approach to argument weight can be usefully applied to the issues addressed by Pinto. I also relate some of these issues to the (...) work of argument theorist Harald Wohlrapp. (shrink)