For the {\it first} type of decision makers we consider, we assume that decisions are rational and well-informed, and that a decision maker with leniency \leniencyValue makes a positive decision only for the \leniencyValue fraction of cases that are most likely to lead to a positive outcome.
%
Specifically, we assume that the decision-makers know the cumulative distribution function $F$ that the risk scores $s =\beta_\obsFeatures\obsFeaturesValue+\beta_\unobservable\unobservableValue$ of defendants follow.
Specifically, we assume that the decision-makers know the cumulative distribution function $F$ that the risk scores $s =b_\obsFeatures\obsFeaturesValue+b_\unobservable\unobservableValue$ of defendants follow.
%
This is a reasonable assumption to make in settings where decision makers have accurate knowledge of the joint feature distribution as such knowledge allows one to calculate $F$.
