Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes

Charlotte s. Vlek; H. Prakken; S. Renooij; Bart Verheij

doi:10.1145/2746090.2746097

Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes

Charlotte s. Vlek
, H. Prakken
, S. Renooij
, Bart Verheij

University of Groningen
External unknown

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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 language	English
Title of host publication	ICAIL '15
Subtitle of host publication	Proceedings of the 15th International Conference on Artificial Intelligence and Law
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	128-137
ISBN (Print)	978-1-4503-3522-5
DOIs	https://doi.org/10.1145/2746090.2746097
Publication status	Published - 2015
Event	15th International Conference on Artificial Intelligence and Law - San Diego, United States Duration: 8 Jun 2015 → 12 Jun 2015

Conference

Conference	15th International Conference on Artificial Intelligence and Law
Country/Territory	United States
City	San Diego
Period	8/06/15 → 12/06/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 16 Peace, Justice and Strong Institutions

Access to Document

10.1145/2746090.2746097

constructingFinal published version, 779 KBLicence: Taverne

Cite this

@inproceedings{e9fcec3ca4954ad9bd3864beafc04401,

title = "Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes",

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).",

author = "Vlek, \{Charlotte s.\} and H. Prakken and S. Renooij and Bart Verheij",

year = "2015",

doi = "10.1145/2746090.2746097",

language = "English",

isbn = "978-1-4503-3522-5",

pages = "128--137",

booktitle = "ICAIL '15",

publisher = "Association for Computing Machinery",

address = "United States",

note = "15th International Conference on Artificial Intelligence and Law ; Conference date: 08-06-2015 Through 12-06-2015",

}

Vlek, CS, Prakken, H , Renooij, S & Verheij, B 2015, Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes. in ICAIL '15: Proceedings of the 15th International Conference on Artificial Intelligence and Law. Association for Computing Machinery, New York, pp. 128-137, 15th International Conference on Artificial Intelligence and Law, San Diego, United States, 8/06/15. https://doi.org/10.1145/2746090.2746097

Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes. / Vlek, Charlotte s.; Prakken, H.; Renooij, S. et al.
ICAIL '15: Proceedings of the 15th International Conference on Artificial Intelligence and Law. New York: Association for Computing Machinery, 2015. p. 128-137.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes

AU - Vlek, Charlotte s.

AU - Prakken, H.

AU - Renooij, S.

AU - Verheij, Bart

PY - 2015

Y1 - 2015

N2 - 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).

AB - 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).

U2 - 10.1145/2746090.2746097

DO - 10.1145/2746090.2746097

M3 - Conference contribution

SN - 978-1-4503-3522-5

SP - 128

EP - 137

BT - ICAIL '15

PB - Association for Computing Machinery

CY - New York

T2 - 15th International Conference on Artificial Intelligence and Law

Y2 - 8 June 2015 through 12 June 2015

ER -

Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes

Abstract

Conference

UN SDGs

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Cite this