Data-driven predictive modeling in risk assessment: Challenges and directions for proper uncertainty representation

Data-driven predictive modeling is increasingly being used in risk assessments. While such modeling may provide improved consequence predictions and probability estimates, it also comes with challenges. One is that the modeling and its output does not measure and represent uncertainty due to lack of knowledge, that is, "epistemic uncertainty." In this article, we demonstrate this point by conceptually linking the main elements and output of data-driven predictive models with the main elements of a general risk description, thereby placing data-driven predictive modeling on a risk science foundation. This allows for an evaluation of such modeling with reference to risk science recommendations for what constitutes a complete risk description. The evaluation leads us to conclude that, as a minimum, to cover all elements of a complete risk description a risk assessment using data-driven predictive modeling needs to be supported by assessments of the uncertainty and risk related to the assumptions underlying the modeling. In response to this need, we discuss an approach for assessing assumptions in data-driven predictive modeling.