A structure-guided approach to capturing bayesian reasoning about legal evidence in argumentation

S. Timmer, J. Meyer, H. Prakken, S. Renooij, Bart Verheij
{"title":"A structure-guided approach to capturing bayesian reasoning about legal evidence in argumentation","authors":"S. Timmer, J. Meyer, H. Prakken, S. Renooij, Bart Verheij","doi":"10.1145/2746090.2746093","DOIUrl":null,"url":null,"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 independencies 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.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2746090.2746093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27

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 independencies 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.
一个结构导向的方法来捕捉贝叶斯推理的法律证据在辩论
在过去的几十年里,法医科学的兴起导致了统计证据的可用性的增加。在法医学背景下,对统计数据和概率进行推理可能是一项不稳定的工作,尤其是在涉及变量之间的独立性时。为了促进对这些证据的正确解释,我们研究了论证模型如何帮助解释统计信息。本文重点研究了论证模型与贝叶斯信念网络之间的联系,后者是一种用于复杂概率信息表示和推理的常用模型。我们引入支持图的概念,作为贝叶斯网络和论证模型之间的中间结构。支持图从贝叶斯网络复杂的图形属性中解脱出来,增强了其直观的解释。此外,我们表明该模型可以为论证分析提供合适的模板。特别是在法律推理的背景下,正确处理统计证据是很重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信