Skip to main content
SpringerLink
Log in

CAUSATI ONT: Modeling Causation in AI&Law

  • Chapter
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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Lehmann, J.: Causation in Artificial Intelligence and Law - A modelling approach. PhD thesis, University of Amsterdam - Faculty of Law - Department of Computer Science and Law (2003)

    Google Scholar 

  2. Lehmann, J., Breuker, J., Brouwer, B.: Causation in ai&law (to appear). AI and Law (2004)

    Google Scholar 

  3. Valente, A.: Legal Knowledge Engineering - A modelling approach. IOS Press, Amsterdam (1995)

    MATH  Google Scholar 

  4. Hart, H., Honore, T.: Causation in the Law. Oxford University Press, Oxford (1985)

    Google Scholar 

  5. Green, L.: Judge and Jury. Kansas City (1930)

    Google Scholar 

  6. Hulswit, M.: A semeiotic account of causation - The cement of the Universe from a Peircean perspective. PhD thesis, Katholieke Universiteit Nijmegen (1998)

    Google Scholar 

  7. Ducasse, C.: On the nature and observability of the causal relation. Journal of Philosophy 23, 57–68 (1926)

    Article  Google Scholar 

  8. Russell, B.: Human Knowledge. Simon and Schuster (1948)

    Google Scholar 

  9. Salmon, W.: Scientific Explanation and the Causal Structure of the World. Princeton University Press, Princeton (1984)

    Google Scholar 

  10. Dowe, P.: Causality and conserved quantities: A reply to salmon. Philosophy of Science 62, 321–333 (1995)

    Article  Google Scholar 

  11. Davidson, D.: Essays on actions and events. Oxford University Press, Oxford (1980)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joost Breuker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bob Brouwer
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

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

Download citation

  • 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)

Publish with us

Policies and ethics

Search

Navigation