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

Sjoerd Timmer, John-Jules Meyer, Henry Prakken, Silja Renooij, Bart Verheij

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.

Manuscript (in PDF-format)

Reference:
Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. The 15th International Conference on Artificial Intelligence and Law (ICAIL 2015). Proceedings of the Conference, 109-118. New York (New York): ACM.

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