What is understandable in Bayesian network explanations?
  Explaining predictions from Bayesian networks, for example to physicians, is
non-trivial. Various explanation methods for Bayesian network inference have
appeared in literature, focusing on different aspects of the underlying
reasoning. While there has been a lot of technical research, there is very
little known about how well humans actually understand these explanations. In
this paper, we present ongoing research in which four different explanation
approaches were compared through a survey by asking a group of human
participants to interpret the explanations.
