Kevin D. Ashley
University of Pittsburgh School of Law, Intelligent Systems Program, and
Learning Research and Development Center
Vincent Aleven
Carnegie Mellon University Human-Computer Interaction
Institute
Abstract
Since the days of Bacon and Galileo, formulating hypotheses
about natural phenomena and testing them against empirical data have been
cornerstones of the natural sciences. As a cognitive framework, hypothesis
formation and testing are also important in legal reasoning. The legal domain,
however, is different from natural science and mathematics in a significant
respect. Determining whether a hypothesized rule and proposed outcome are
consistent with past legal decisions is much more a matter of interpretation.
The aims of this project are to (1) design and evaluate an Artificial
Intelligence (AI) cognitive model of framing and testing hypotheses in an
interpretive domain, legal reasoning, and (2) incorporate the model in an
intelligent tutoring system (ITS) to teach law students the process.
The project builds upon two recent developments: (1) a newly
invented means to frame and evaluate hypotheses predicting the outcomes of new
cases based on an AI database of existing precedents; (2) a convenient, on-line
corpus of U.S. Supreme Court oral arguments in aural and written form,
including many concrete examples of legal hypothesis framing and testing. In
response to an advocate’s proposed hypothesis of how the case should be
decided, the Justices often challenge it by posing hypotheticals, sometimes
forcing the advocate to modify or abandon the hypothesis.
By studying these examples, the researchers, participating law
students and law faculty will schematize and model the process of framing and
testing legal hypotheses, implement it computationally, evaluate it
empirically, and use it to design the ITS.
The tutor will implement the model in various legal domains,
each with a body of legal rules, issues, precedents, and principles,
operationalized in a way that supports hypothesis formulation, prediction,
testing, and explanation. Using the model, it will guide and challenge
students’ arguments. It will predict outcomes of cases, help students construct
tests and rationales justifying the prediction, and help them evaluate the
hypothesis by posing or responding to hypothetical challenges.
The researchers will evaluate the project’s success in terms of:
(1) the accuracy of the model’s predictions for new cases and the extent it
improves case retrieval; (2) how well model-generated arguments compare to
those in the Supreme Court oral arguments or generated by law students; (3) how
well ITS-trained students compare to a control group taught the same process
using conventional law school methods; (4) whether ITS-trained students
generate more accurate self-explanations of the Supreme Court oral arguments.
This work extends AI techniques to a much less well-structured domain than natural
science and mathematics, one more like the common sense domains AI has yet to
address. By using AI to investigate empirically a cognitive phenomenon,
framing and testing hypotheses in an interpretive domain, it will contribute to
research in AI & Law, Case-based Reasoning, AI & Education, and
Cognitive Science.
* NSF Grant IIS-0412830, 2004 - 2009, in the
amount of $650,000.