Explaining Bayesian Networks in Natural Language using Factor Arguments. Evaluation in the medical domain.

In this paper, we propose a model for building natural language explanations for Bayesian Network
Reasoning in terms of factor arguments, which are argumentation graphs of flowing evidence, relating
the observed evidence to a target variable we want to learn about. We introduce the notion of factor
argument independence to address the outstanding question of defining when arguments should be
presented jointly or separately and present an algorithm that, starting from the evidence nodes and a
target node, produces a list of all independent factor arguments ordered by their strength. Finally, we
implemented a scheme to build natural language explanations of Bayesian Reasoning using this approach.
Our proposal has been validated in the medical domain through a human-driven evaluation study where
we compare the Bayesian Network Reasoning explanations obtained using factor arguments with an
alternative explanation method. Evaluation results indicate that our proposed explanation approach
is deemed by users as significantly more useful for understanding Bayesian Network Reasoning than
another existing explanation method it is compared to.