  In prediction-based decision-making systems, different perspectives can be at
odds: The short-term business goals of the decision makers are often in
conflict with the decision subjects' wish to be treated fairly. Balancing these
two perspectives is a question of values. However, these values are often
hidden in the technicalities of the implementation of the decision-making
system. In this paper, we propose a framework to make these value-laden choices
clearly visible. We focus on a setting in which we want to find decision rules
that balance the perspective of the decision maker and of the decision
subjects. We provide an approach to formalize both perspectives, i.e., to
assess the utility of the decision maker and the fairness towards the decision
subjects. In both cases, the idea is to elicit values from decision makers and
decision subjects that are then turned into something measurable. For the
fairness evaluation, we build on well-known theories of distributive justice
and on the algorithmic literature to ask what a fair distribution of utility
(or welfare) looks like. This allows us to derive a fairness score that we then
compare to the decision maker's utility. As we focus on a setting in which we
are given a trained model and have to choose a decision rule, we use the
concept of Pareto efficiency to compare decision rules. Our proposed framework
can both guide the implementation of a decision-making system and help with
audits, as it allows us to resurface the values implemented in a
decision-making system.
