Demonstration of a Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in ArgumentationSjoerd Timmer, John-Jules Meyer, Henry Prakken, Silja Renooij, Bart VerheijReasoning 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 in- crease 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 statis- tical 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 net- work but disentangles the complicating graphical properties of such models and instead emphasises its intuitive under- standing. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.Manuscript (in PDF-format) Reference: Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). Demonstration of 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, 233-234. New York (New York): ACM. Bart Verheij's home page - research - publications |