Causation in AI and law
Artificial Intelligence and Law 12 (4):279-315 (2004)
| 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. Such example consists of: a definition of the legal concept of responsibility (in terms of liability and accountability); a definition of the legal concept of causation in fact (in terms of the initiation of physical processes by an agent and of the provision of reasons and/or opportunities to other agents); 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 (in particular, the concepts of category, dimension, object, agent, process, event and act). | |||||||||
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Christoph Hoerl (2011). Causal Reasoning. Philosophical Studies 152 (2):167-179.
Jonathan Schaffer (2012). Disconnection and Responsibility. Legal Theory 18 (Special Issue 04):399-435.
Rinke Hoekstra & Joost Breuker (2007). Commonsense Causal Explanation in a Legal Domain. Artificial Intelligence and Law 15 (3):281-299.
Jonathan Schaffer (2001). Causes as Probability Raisers of Processes. Journal of Philosophy 98 (2):75-92.
Alex Broadbent (2009). Fact and Law in the Causal Inquiry. Legal Theory 15:173-191.
Jos Lehmann & Aldo Gangemi (2007). An Ontology of Physical Causation as a Basis for Assessing Causation in Fact and Attributing Legal Responsibility. Artificial Intelligence and Law 15 (3):301-321.
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