Characterization of biases and their impact on the integrity of a risk study

Several recent publications have addressed the issue of risk study quality, which often calls for the minimization of biases. However, biases are prevalent and are often unavoidable in any analytic and decision-making domain. There remains little guidance on how to identify and address potential biases and how those biases can influence a risk study. This paper investigates biases related to systematic error, inclusion of events, models, and cognitive factors as they relate to the characterization of risk. With this understanding, we explore how the risk analyst can acknowledge and address those biases in support of a high-quality risk study. New insights are obtained by considering the biases in relation to the basic elements of a risk characterization: events and consequence, uncertainty measurements and descriptions, and the supporting knowledge. This paper will be of interest to risk analysts, policymakers, and other stakeholders for risk study applications.

Automated summary

This paper examines biases in risk studies and investigates how to identify and address them to ensure high-quality risk analysis. By considering biases related to systematic error, event inclusion, models, and cognitive factors, the paper explores their influence on risk characterization. The insights gained from this exploration can be valuable to risk analysts, policymakers, and other stakeholders involved in risk study applications.