  Recent work on algorithmic fairness has largely focused on the fairness of
discrete decisions, or classifications. While such decisions are often based on
risk score models, the fairness of the risk models themselves has received
considerably less attention. Risk models are of interest for a number of
reasons, including the fact that they communicate uncertainty about the
potential outcomes to users, thus representing a way to enable meaningful human
oversight. Here, we address fairness desiderata for risk score models. We
identify the provision of similar epistemic value to different groups as a key
desideratum for risk score fairness. Further, we address how to assess the
fairness of risk score models quantitatively, including a discussion of metric
choices and meaningful statistical comparisons between groups. In this context,
we also introduce a novel calibration error metric that is less sample
size-biased than previously proposed metrics, enabling meaningful comparisons
between groups of different sizes. We illustrate our methodology - which is
widely applicable in many other settings - in two case studies, one in
recidivism risk prediction, and one in risk of major depressive disorder (MDD)
prediction.

Machine Learning;Computers and Society;Applications;Methodology;Machine Learning