  Product risk assessment is the overall process of determining whether a
product, which could be anything from a type of washing machine to a type of
teddy bear, is judged safe for consumers to use. There are several methods used
for product risk assessment, including RAPEX, which is the primary method used
by regulators in the UK and EU. However, despite its widespread use, we
identify several limitations of RAPEX including a limited approach to handling
uncertainty and the inability to incorporate causal explanations for using and
interpreting test data. In contrast, Bayesian Networks (BNs) are a rigorous,
normative method for modelling uncertainty and causality which are already used
for risk assessment in domains such as medicine and finance, as well as
critical systems generally. This article proposes a BN model that provides an
improved systematic method for product risk assessment that resolves the
identified limitations with RAPEX. We use our proposed method to demonstrate
risk assessments for a teddy bear and a new uncertified kettle for which there
is no testing data and the number of product instances is unknown. We show
that, while we can replicate the results of the RAPEX method, the BN approach
is more powerful and flexible.
