A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation

S.T. Timmer; J.J.C. Meyer; H. Prakken; S. Renooij; Bart Verheij

doi:10.1145/2746090.2746093

A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation

S.T. Timmer, J.J.C. Meyer, H. Prakken, S. Renooij, Bart Verheij

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

Abstract

Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when in-
dependencies between variables are involved. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks,
the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the correct treatment of statistical
evidence is important.

Original language	English
Title of host publication	ICAIL '15
Subtitle of host publication	Proceedings of the 15th International Conference on Artificial Intelligence and Law
Publisher	Association for Computing Machinery
Pages	109-118
ISBN (Print)	978-1-4503-3522-5
DOIs	https://doi.org/10.1145/2746090.2746093
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

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10.1145/2746090.2746093

structureFinal published version, 698 KBLicence: Taverne

Cite this

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title = "A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation",

abstract = "Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when in-dependencies between variables are involved. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks,the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the correct treatment of statisticalevidence is important.",

author = "S.T. Timmer and J.J.C. Meyer and H. Prakken and S. Renooij and Bart Verheij",

year = "2015",

doi = "10.1145/2746090.2746093",

language = "English",

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pages = "109--118",

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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",

}

Timmer, ST, Meyer, JJC , Prakken, H , Renooij, S & Verheij, B 2015, A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. in ICAIL '15: Proceedings of the 15th International Conference on Artificial Intelligence and Law. Association for Computing Machinery, pp. 109-118, 15th International Conference on Artificial Intelligence and Law, San Diego, United States, 8/06/15. https://doi.org/10.1145/2746090.2746093

A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. / Timmer, S.T.; Meyer, J.J.C.; Prakken, H. et al.
ICAIL '15: Proceedings of the 15th International Conference on Artificial Intelligence and Law. Association for Computing Machinery, 2015. p. 109-118.

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

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T1 - A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation

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AU - Prakken, H.

AU - Renooij, S.

AU - Verheij, Bart

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AB - Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when in-dependencies between variables are involved. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks,the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the correct treatment of statisticalevidence is important.

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DO - 10.1145/2746090.2746093

M3 - Conference contribution

SN - 978-1-4503-3522-5

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EP - 118

BT - ICAIL '15

PB - Association for Computing Machinery

T2 - 15th International Conference on Artificial Intelligence and Law

Y2 - 8 June 2015 through 12 June 2015

ER -

A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation

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