Argumentation schemes for legal presumption of causality
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ICAIL '23 : proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, New York : Association for Computing Machinery (ACM), 2023, pp. 157-166
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LIEPIŅA, Rūta, WYNER, Adam, SARTOR, Giovanni, LAGIOIA, Francesca, Argumentation schemes for legal presumption of causality, in ICAIL ’23 : proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, New York : Association for Computing Machinery (ACM), 2023, pp. 157-166 - https://hdl.handle.net/1814/78102
Abstract
Causal reasoning is a challenging topic not only in philosophy, science and in theories of human mind, but also in legal reasoning. Causality is indeed a key precondition for civil and criminal liability, in all cases dealing with the connection between human actions or omissions and harmful events. Only a partial overlap exists between natural causality (cause-in-fact) and legal causality: there are instances in which what appears to be a natural cause is not recognised as a legal one, as well as instances in which causality may be presumptively ascribed by the law in the absence of decisive evidence for natural causality. Legal policy considerations may explain these puzzling divergences, as we will discuss in the following. In this paper, we use argumentation schemes to provide simple and intuitive patterns for assessing causality in the legal domain. The analysis of these argument schemes will enable us to clarify some connections between natural and legal causation. Our schemes will include the necessary condition (but-for), overdetermination (NESS), preemption, presumptions based on the increase of risks or presumption based on statutory obligations, and interruption of causality due to unexpected events (Actus Novus). These approaches are tested on the basis of real legal cases in different domains.
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Published online: 07 September 2023
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European Commission, 833647
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This work has been partially funded by the European Union’s Justice programme under grant agreement 101007420 (ADELE), by the European Horizon 2020 ERC project CompuLaw (Computable Law) under grant agreement 833647, by the Italian Ministry of Education and Research’s PRIN programme under grant agreement 2017NCPZ22 (LAILA), by the European Commission under the NextGeneration EU programme, PNRR- M4C2- Investimento 1.3, Partenariato Esteso PE00000013- “FAIR- Future Artificial Intelligence Research”- Spoke 8 “Pervasive AI”.