research-article

Supporting Discussions About Forensic Bayesian Networks Using Argumentation

Authors:

Silja RenooijAuthors Info & Claims

ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

Pages 143 - 152

https://doi.org/10.1145/3322640.3326710

Published: 17 June 2019 Publication History

Abstract

Bayesian networks (BNs) are powerful tools that are increasingly being used by forensic and legal experts to reason about the uncertain conclusions that can be inferred from the evidence in a case. Although in BN construction it is good practice to document the model itself, the importance of documenting design decisions has received little attention. Such decisions, including the (possibly conflicting) reasons behind them, are important for legal experts to understand and accept probabilistic models of cases. Moreover, when disagreements arise between domain experts involved in the construction of BNs, there are no systematic means to resolve such disagreements. Therefore, we propose an approach that allows domain experts to explicitly express and capture their reasons pro and con modelling decisions using argumentation, and that resolves their disagreements as much as possible. Our approach is based on a case study, in which the argumentation structure of an actual disagreement between two forensic BN experts is analysed.

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Cited By

Araszkiewicz MBench-Capon TFrancesconi ELauritsen MRotolo A(2022)Thirty years of Artificial Intelligence and Law: overviewsArtificial Intelligence and Law10.1007/s10506-022-09324-930:4(593-610)Online publication date: 6-Aug-2022
https://doi.org/10.1007/s10506-022-09324-9
Wieten RBex FPrakken HRenooij S(2021)Information graphs and their use for Bayesian network graph constructionInternational Journal of Approximate Reasoning10.1016/j.ijar.2021.06.007Online publication date: Jun-2021
https://doi.org/10.1016/j.ijar.2021.06.007
Wieten RBex FPrakken HRenooij S(2019)Constructing Bayesian Network Graphs from Labeled ArgumentsSymbolic and Quantitative Approaches to Reasoning with Uncertainty10.1007/978-3-030-29765-7_9(99-110)Online publication date: 4-Sep-2019
https://doi.org/10.1007/978-3-030-29765-7_9

Index Terms

Supporting Discussions About Forensic Bayesian Networks Using Argumentation

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cover image ACM Conferences

ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

June 2019

312 pages

ISBN:9781450367547

DOI:10.1145/3322640

Copyright © 2019 ACM.

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Published: 17 June 2019

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ICAIL '19

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ICAIL '19: Seventeenth International Conference on Artificial Intelligence and Law

June 17 - 21, 2019

QC, Montreal, Canada

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Cited By

Araszkiewicz MBench-Capon TFrancesconi ELauritsen MRotolo A(2022)Thirty years of Artificial Intelligence and Law: overviewsArtificial Intelligence and Law10.1007/s10506-022-09324-930:4(593-610)Online publication date: 6-Aug-2022
https://doi.org/10.1007/s10506-022-09324-9
Wieten RBex FPrakken HRenooij S(2021)Information graphs and their use for Bayesian network graph constructionInternational Journal of Approximate Reasoning10.1016/j.ijar.2021.06.007Online publication date: Jun-2021
https://doi.org/10.1016/j.ijar.2021.06.007
Wieten RBex FPrakken HRenooij S(2019)Constructing Bayesian Network Graphs from Labeled ArgumentsSymbolic and Quantitative Approaches to Reasoning with Uncertainty10.1007/978-3-030-29765-7_9(99-110)Online publication date: 4-Sep-2019
https://doi.org/10.1007/978-3-030-29765-7_9

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