Computerized algorithms for predicting the outcomes of legal problems can extract and present information from particular databases of cases to guide the legal analysis of new problems. They can have practical value despite the limitations that make reliance on predictions risky for other real-world purposes such as estimating settlement values. An algorithm's ability to generate reasonable legal arguments also is important. In this article, computerized prediction algorithms are compared not only in terms of accuracy, but also in terms of their ability to explain predictions and to integrate predictions and arguments. Our approach, the Issue-Based Prediction algorithm, is a program that tests hypotheses about how issues in a new case will be decided. It attempts to explain away counterexamples inconsistent with a hypothesis, while apprising users of the counterexamples and making explanatory arguments based on them.
Kevin D. Ashley & Stephanie Bruninghaus,
Computer Models for Legal Prediction,
Available at: https://scholarship.law.pitt.edu/fac_articles/526
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