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

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 independences 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.

A method for explaining Bayesian networks for legal evidence with scenarios

“…However, the scenarios used in Druzdzel (1990) consists of configurations of nodes and satisfy no further narrative properties such as completeness, consistency and plausibility. In particular, in Druzdzel (1990), the following would be considered a scenario, while in our work it is not: Jane had a knife = F, Jane stabbed Mark = F, Mark died = T. Other work specifically related to explaining Bayesian networks for legal cases is by Timmer et al (2014Timmer et al ( , (2015 and Keppens (2012), who both extract arguments from Bayesian networks. In particular, Timmer et al (2014) also uses a similar measure to ours to calculate evidential support.…”

“…Typically, three distinct approaches are used to formalize reasoning with legal evidence: the narrative approach, the probabilistic approach and the argumentative approach. Combinations of these approaches have been studied by Bex (2011), who combined arguments and scenarios, by Timmer et al (2015), who considered Bayesian networks and arguments, and by Verheij (2014), who combined arguments, scenarios and probabilities. An overview of these combined approaches can be found in Verheij et al (2015).…”

HYPO’S legacy: introduction to the virtual special issue

“…The methods used here will be the standard ways in which people reason about the world: evidence takes many forms: witness testimony, documents, forensics, video footage etc. Each of these are associated with their own kind of reasoning, coherence (Bex 2011), probability (Timmer et al 2015), etc, and the courts should make use of these established ways of reasoning. There is nothing distinctively legal in this phase, and the decisions are often made by lay juries, and often facts cannot be revisited at the Appeal stage.…”

Explaining Bayesian Networks Using Argumentation

“…However, exhaustively enumerating every possible probabilistic rule and argument is computationally infeasible and also not necessary because many of the enumerated antecedents will never be met, and many arguments constructed in this way are superfluous because they argue for irrelevant conclusions. In a report [11] we proposed a new method that solves these issues. We proposed to split the process of argument generation into two phases: from the BN we construct a support graph at first, from which argument can be generated in a second phase.…”