A causal Bayesian network approach for consumer product safety and risk assessment

Abstract

Introduction: Product risk assessment is the overall process of determining whether a product is judged safe for consumers to use. Among several methods for product risk assessment, RAPEX is the primary one used by regulators in the UK and EU. Despite its widespread use we identify several limitations of RAPEX, including a limited approach to handling uncertainty, especially in the absence of data, and the inability to incorporate causal explanations for using and interpreting the data. Method: We develop a Bayesian Network (BN) model to provide an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. 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. Results: We use the BN approach to demonstrate risk assessments for products where relevant test and product instance data are and are not available. Whereas RAPEX can only produce results given relevant data, the BN approach produce results for products with and with no relevant data – replicating RAPEX in the former and providing deeper insights in both cases. Conclusion: The BN approach is powerful and flexible for systematic product risk assessment. While it can complement more traditional methods like RAPEX, it is able to provide quantified, auditable assessments in situations where such methods cannot because of lack of data. Practical Applications: Safety regulators, manufacturers, and risk professionals can use the BN approach for all types of consumer product risk assessment, including for novel products or products with little or no historical data. They can also use it to validate the results of existing methods when data becomes available. It informs risk management decisions and helps understand the effect of those decisions on the consumer risk perception.