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CAUSATI ONT: Modeling Causation in AI&Law

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Law and the Semantic Web

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3369))

Abstract

Reasoning about causation in fact is an essential element of attributing legal responsibility. Therefore, the automation of the attribution of legal responsibility requires a modelling effort aimed at the following: a thorough understanding of the relation between the legal concepts of responsibility and of causation in fact; a thorough understanding of the relation between causation in fact and the common sense concept of causation; and, finally, the specification of an ontology of the concepts that are minimally required for (automatic) common sense reasoning about causation. This article offers a worked out example of the indicated analysis, which comprises: a definition of the legal concept of responsibility; a definition of the legal concept of causation in fact; CausatiOnt, an AI-like ontology of the common sense (causal) concepts that are minimally needed for reasoning about the legal concept of causation in fact.

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Author information

Authors and Affiliations

  1. Laboratory for Applied Ontology, Institute of Cognitive Science and Technology, Italian National Research Council, Rome, Italy

    Jos Lehmann

  2. Leibniz Center For Law, Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands

    Joost Breuker

  3. Department of Jurisprudence, Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands

    Bob Brouwer

Authors
  1. Jos Lehmann
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  2. Joost Breuker
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  3. Bob Brouwer
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Editor information

Editors and Affiliations

  1. iSOCO, P.O. Box, Spain

    V. Richard Benjamins

  2. Institute of Law and Technology, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain

    Pompeu Casanovas

  3. Leibniz Center for Law, University of Amsterdam,  

    Joost Breuker

  4. Institute for Cognitive Sciences and Technology, Italian Research Council (ISTC-CNR), Via Nomentana 56, 00161, Rome, Italy

    Aldo Gangemi

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© 2005 Springer-Verlag Berlin Heidelberg

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Lehmann, J., Breuker, J., Brouwer, B. (2005). CAUSATI ONT: Modeling Causation in AI&Law. In: Benjamins, V.R., Casanovas, P., Breuker, J., Gangemi, A. (eds) Law and the Semantic Web. Lecture Notes in Computer Science(), vol 3369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32253-5_6

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  • DOI: https://doi.org/10.1007/978-3-540-32253-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25063-0

  • Online ISBN: 978-3-540-32253-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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