Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes

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Abstract

In a criminal trial, a judge or jury needs to reach a conclusion
about ‘what happened’ based on the available evidence. Often
this includes probabilistic evidence. Whereas Bayesian
networks form a good tool for analysing evidence probabilistically,
simply presenting the outcome of the network to
a judge or jury does not allow them to make an informed
decision. In this paper, we propose to combine Bayesian
networks with a narrative approach to reasoning with legal
evidence, the result of which allows a juror to reason with
alternative scenarios while also incorporating probabilistic
information. The proposed method aids both the construction
and the understanding of Bayesian networks, using scenario
schemes. We make three distinct contributions: (1) we
propose to use scenario schemes to aid the construction of
Bayesian networks, (2) we propose a method for producing
scenarios in text form from the resulting networks and (3)
we propose a format for reporting the alternative scenarios
and their relations to the evidence (including strength).
Original languageEnglish
Title of host publicationProceedings of the 15th International Conference on Artificial Intelligence and Law
Place of PublicationNew York, USA
PublisherACM Press
Pages128-137
ISBN (Print) 978-1-4503-3522-5
DOIs
Publication statusPublished - 8-Jun-2015
Event15th International Conference on Artificial Intelligence and Law (ICAIL 2015) - San Diego, United States
Duration: 8-Jun-201512-Jun-2015

Conference

Conference15th International Conference on Artificial Intelligence and Law (ICAIL 2015)
CountryUnited States
CitySan Diego
Period08/06/201512/06/2015

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