Despite an increase in the usage of AI models in various domains, the reasoning behind the decisions of complex models may remain unclear to the end-user. Understanding why a model entails specific conclusions is crucial in many domains. A natural example of this need for explainability can be drawn from the use of a medical diagnostic system, where it combines patient history, symptoms and test results in a sophisticated way, estimate the probability that a patient has cancer, and give probabilistic prognoses for different treatment options. In order to accept the result, the patient, the doctor, and even the model developers want to understand (and should be told) why the model supports these conclusions. Unfortunately, decades of bitter experience show that the resulting models and reasoning are often too complex for even domain experts, let alone ordinary users, to understand unaided.

Bayesian Networks (BNs) have become popular as a probabilistic AI tool for working with complex problems, especially in the field of medical and law. A Bayesian network consists of a graph and probability tables, together representing a joint probability distribution. From a Bayesian network, any probability of interest can be computed. The graphical structure contains information about (in)dependencies between variables, which makes a Bayesian network particularly suitable for modelling complex relations between variables.