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Explaining predictions from Bayesian networks, for example to physicians, isnon-trivial. Various explanation methods for Bayesian network inference haveappeared in literature, focusing on different aspects of the underlyingreasoning. While there has been a lot of technical research, there is verylittle known about how well humans actually understand these explanations. Inthis paper, we present ongoing research in which four different explanationapproaches were compared through a survey by asking a group of humanparticipants to interpret the explanations.

What is understandable in Bayesian network explanations?