A two-phase method for extracting explanatory arguments from Bayesian networks

Errors in reasoning about probabilistic evidence can have severe consequences.
In the legal domain a number of recent miscarriages of justice emphasises how
severe these consequences can be. These cases, in which forensic evidence was
misinterpreted, have ignited a scientific debate on how and when probabilistic
reasoning can be incorporated in (legal) argumentation. One promising approach
is to use Bayesian networks (BNs), which are well-known scientific models for
probabilistic reasoning. For non-statistical experts, however, Bayesian networks
may be hard to interpret. Especially since the inner workings of Bayesian
networks are complicated, they may appear as black box models. Argumentation
models, on the contrary, can be used to show how certain results are derived
in a way that naturally corresponds to everyday reasoning. In this paper we
propose to explain the inner workings of a BN in terms of arguments.
We formalise a two-phase method for extracting probabilistically supported
arguments from a Bayesian network. First, from a Bayesian network we construct
a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the relation between hypotheses and evidence that is modelled in the Bayesian network.