Explaining legal Bayesian networks using support graphs

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

Legal reasoning about evidence can be a precarious exercise, in particular when statistics are involved. A number of recent miscarriages of justice have provoked a scientific interest in formal models of legal evidence. Two such models are presented by Bayesian networks (BNs) and argumentation. A limitation of argumentation is that it is difficult to embed probabilities. BNs, on the other hand, are probabilistic by nature. A disadvantage of BNs is that it can be hard to explain what is modelled and how the results came about. Assuming that a forensic expert presents evidence in a way that is either already a BN or expressed in terms that easily map to a simple BN, we may wish to express the same information in argumentative terms. We address this issue by translating Bayesian networks to arguments. We do this by means of an intermediate structure, called a support graph, which represents the variables from the Bayesian network, maintaining independence information in the network, but connected in a way that more closely resembles argumentation. In the current paper we test the support graph method on a Bayesian network from the literature. We argue that the resulting support graph adequately captures the possible arguments about the represented case. In addition, we highlight strengths and limitations of the method that are revealed by this case study.

Manuscript (in PDF-format)

Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). Explaining legal Bayesian networks using support graphs. Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (ed. Rotolo, A.), 121-130. Amsterdam: IOS Press.

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