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    Using Bayesian networks to guide the assessment of new evidence in an appeal case. 
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    Using Bayesian networks to guide the assessment of new evidence in an appeal case.

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    Published Version (1.748Mb)
    Volume
    5
    Pagination
    9 - ?
    DOI
    10.1186/s40163-016-0057-6
    Journal
    Crime Sci
    ISSN
    2193-7680
    Metadata
    Show full item record
    Abstract
    When new forensic evidence becomes available after a conviction there is no systematic framework to help lawyers to determine whether it raises sufficient questions about the verdict in order to launch an appeal. This paper presents such a framework driven by a recent case, in which a defendant was convicted primarily on the basis of audio evidence, but where subsequent analysis of the evidence revealed additional sounds that were not considered during the trial. The framework is intended to overcome the gap between what is generally known from scientific analyses and what is hypothesized in a legal setting. It is based on Bayesian networks (BNs) which have the potential to be a structured and understandable way to evaluate the evidence in a specific case context. However, BN methods suffered a setback with regards to the use in court due to the confusing way they have been used in some legal cases in the past. To address this concern, we show the extent to which the reasoning and decisions within the particular case can be made explicit and transparent. The BN approach enables us to clearly define the relevant propositions and evidence, and uses sensitivity analysis to assess the impact of the evidence under different assumptions. The results show that such a framework is suitable to identify information that is currently missing, yet clearly crucial for a valid and complete reasoning process. Furthermore, a method is provided whereby BNs can serve as a guide to not only reason with incomplete evidence in forensic cases, but also identify very specific research questions that should be addressed to extend the evidence base and solve similar issues in the future.
    Authors
    Smit, NM; Lagnado, DA; Morgan, RM; Fenton, NE
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/12634
    Collections
    • Electronic Engineering and Computer Science [2816]
    Language
    eng
    Licence information
    © Smit et al 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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