Product safety idioms: a method for building causal Bayesian networks for product safety and risk assessment

TL;DR

This paper introduces product safety idioms, small reusable Bayesian network fragments, to construct causal models for product safety and risk assessment, effectively combining data and knowledge even with limited testing data.

Contribution

It presents a novel set of product safety idioms that enable building comprehensive causal Bayesian networks for safety evaluation using minimal data.

Findings

Idioms are effective for modeling product safety and risk.

Models can be built with limited or no product testing data.

Applicable to a wide range of products.

Abstract

Idioms are small, reusable Bayesian network (BN) fragments that represent generic types of uncertain reasoning. This paper shows how idioms can be used to build causal BNs for product safety and risk assessment that use a combination of data and knowledge. We show that the specific product safety idioms that we introduce are sufficient to build full BN models to evaluate safety and risk for a wide range of products. The resulting models can be used by safety regulators and product manufacturers even when there are limited (or no) product testing data.