CAUSATIONT: modeling causation in AI&law

Authors:
Jos Lehmann;Joost Breuker;Bob Brouwer
Affiliations:
Laboratory for Applied Ontology, Institute of Cognitive Science and Technology, Italian National Research Council, Rome, Italy;Leibniz Center For Law, Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands;Department of Jurisprudence, Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands
Venue:
Law and the Semantic Web
Year:
2005

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Developing Content for LKIF: Ontologies and Frameworks for Legal Reasoning

Proceedings of the 2006 conference on Legal Knowledge and Information Systems: JURIX 2006: The Nineteenth Annual Conference

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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.