Demonstration of a structure-guided approach to capturing bayesian reasoning about legal evidence in argumentation

S. Timmer, J. Meyer, H. Prakken, S. Renooij, Bart Verheij
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引用次数: 3

Abstract

Reasoning about statistics and probabilities can, when not treated with cautiousness, lead to reasoning errors. Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. Uncertainties are by forensic experts often expressed numerically, but lawyers, judges and other legal experts have notorious difficulty interpreting these results [3, 1, 2, 5]. In this demonstration of our main paper [6] we focus on the connection between formal models of argumentation and Bayesian belief networks (BNs). We use BNs because they are a well-known 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 captures the inferences modelled in a Bayesian network but disentangles the complicating graphical properties of such models and instead emphasises its intuitive understanding. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.
示范一种结构导向的方法,在辩论中捕捉关于法律证据的贝叶斯推理
关于统计和概率的推理,如果不谨慎对待,可能会导致推理错误。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。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。法医专家通常用数字来表达不确定性,但律师、法官和其他法律专家在解释这些结果方面存在着众所周知的困难[3,1,2,5]。在我们的主要论文[6]的演示中,我们专注于论证的形式模型和贝叶斯信念网络(BNs)之间的联系。我们使用神经网络是因为它们是一个众所周知的模型来表示和推理复杂的概率信息。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。支持图捕获了在贝叶斯网络中建模的推论,但它理清了这些模型复杂的图形属性,而是强调了其直观的理解。此外,我们还证明了这个中间模型可以作为模板来生成基于数据的不同参数。
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