Toward a practical legal rule revision in legal debugging

Abstract

Computational law researchers have long been interested in developing machine legal reasoning systems and machine-readable rule-bases to simulate legal reasoning given by judges. Basically, legal rule-bases rely on literal interpretations of statutes. In practice, however, literal interpretation of statute can have counterintuitive consequences. To deal with such consequences, English courts (the courts of England and Wales) have developed rules of judicial interpretation for reinterpreting statutes i.e. choosing or modifying the meaning of the statute’s wording (golden rule) or determining the statute’s purpose (mischief rule), and these rules have not been investigated in computational law. Previous research proposed the concept of legal debugging to formalize detection and resolution of counterintuitive consequences in legal rule-bases. Legal debugging consists of two steps. The first step is culprit detection, which involves interacting with a judge in order to identify a culprit, which is intuitively a part of statute that can be determined as a root cause of counterintuitive consequences. The second step is culprit resolution, which involves revising the rule-base to resolve a detected culprit. In this paper, we match two inductive logic programming approaches to two rules of judicial interpretation. The first approach is a bottom-up approach, which involves identifying an exceptional situation of the case and generalizing the legal rule revision. We match the bottom-up approach to the golden rule by using knowledge bases in the form of legal ontologies. The second approach is a top-down approach, which involves determining the purpose of the statute and refining the legal rule revision so that the revision fits within the context of the statute. We match the top-down approach to the mischief rule by using knowledge bases in the form of metarules. By using these matches, legal debugging can perform more practical legal revisions during the culprit resolution step.