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A goal-dependent abstraction for legal reasoning by analogy

Published: 01 March 1997 Publication History

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Joseph S. Fulda

Legal cases are decided by precedent, but few precedents make the case at bar <__?__Pub Fmt italic>res judicata.<__?__Pub Fmt /italic><__?__Pub Caret> Instead, analogical reasoning is used, together with canons of statutory interpretation and theories of constitutional jurisprudence. This paper provides a model and algorithm for analogical reasoning in the legal context. The model is best explained by the example used throughout the paper, the rule that vehicles are not allowed in a public park. How do we reason analogically that horses are not allowed in a public park__?__ The answer given by the paper is to look at the properties of a vehicle that form the ground for the rule (being larger than a human being and being mobile, hence being dangerous) and checking the case being considered to see whether the properties and relations that form the ground of the rule apply there, too. In the case of a horse, although it is not a vehicle, it is large and mobile, hence potentially dangerous, so we conclude that it is not permitted in a public park. Since the method seeks the reason for the rule and abstracts it out, it is called goal-dependent abstraction. To do this, a higher-level entity is postulated, whose properties include all those known to be covered by the rule. The new entity is then checked to see whether it is an instance of the postulated entity. If it has the properties, it is taken to be an instance of the higher-level entity, and the properties are assumed to have been inherited. The authors call this process “generalization and deduction.” In addition to having an annoying number of missing articles, misplaced modifiers, and failures of agreement, the paper uses an ordinance rather than cases for analogical reasoning, a practice that makes little sense unless the legislature is always perfectly consistent. Thus, buses and maintenance vehicles may be allowed, despite the ground, and horses may have been overlooked, despite the ground. There is also an assumption that laws do not originate from pressure by interest groups but from well-defined, broadly applicable reasons. Such a system could not, for example, distinguish licensed vehicles from unlicensed vehicles if the purpose of the licensing system were to raise funds and limit competition. In summary, the paper's methodology is appropriate for judicial reasoning from prior cases, but fails in its apparent application as a means of interpreting ordinances and statutes.

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cover image Artificial Intelligence and Law
Artificial Intelligence and Law  Volume 5, Issue 1-2
March 1997
42 pages

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Kluwer Academic Publishers

United States

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Published: 01 March 1997

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  • (2003)Learning‐Assisted Automated PlanningAI Magazine10.1609/aimag.v24i2.170524:2(73-96)Online publication date: 1-Jun-2003
  • (1999)AI watchACM SIGCAS Computers and Society10.1145/382018.38203829:2(31)Online publication date: 1-Jun-1999
  • (1999)A demonstration of a legal reasoning system based on teleological analogiesProceedings of the 7th international conference on Artificial intelligence and law10.1145/323706.323798(196-205)Online publication date: 14-Jun-1999
  • (1997)Attacking legal argument by examining stability of case citation with goal-dependent abstractionProceedings of the 6th international conference on Artificial intelligence and law10.1145/261618.261652(190-197)Online publication date: 30-Jun-1997

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