@inproceedings{7c52ef791fb14b5bb3a658ef48626807,
title = "Exploiting Causality in Constructing Bayesian Network Graphs from Legal Arguments",
abstract = "In this paper, we propose a structured approach for transforming legal arguments to a Bayesian network (BN) graph. Our approach automatically constructs a fully specified BN graph by exploiting causality information present in legal arguments. Moreover, we demonstrate that causality information in addition provides for constraining some of the probabilities involved. We show that for undercutting attacks it is necessary to distinguish between causal and evidential attacked inferences, which extends on a previously proposed solution to modelling undercutting attacks in BNs. We illustrate our approach by applying it to part of an actual legal case, namely the Sacco and Vanzetti legal case.",
keywords = "Bayesian networks, legal reasoning, argumentation, causality",
author = "G.M. Wieten and F.J. Bex and H. Prakken and S. Renooij",
year = "2018",
doi = "10.3233/978-1-61499-935-5-151",
language = "English",
isbn = "978-1-61499-934-8",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "151--160",
editor = "Palmirani, {Monica }",
booktitle = "Legal Knowledge and Information Systems",
address = "Netherlands",
}