  Scoring systems, as a type of predictive model, have significant advantages
in interpretability and transparency and facilitate quick decision-making. As
such, scoring systems have been extensively used in a wide variety of
industries such as healthcare and criminal justice. However, the fairness
issues in these models have long been criticized, and the use of big data and
machine learning algorithms in the construction of scoring systems heightens
this concern. In this paper, we propose a general framework to create
fairness-aware, data-driven scoring systems. First, we develop a social welfare
function that incorporates both efficiency and group fairness. Then, we
transform the social welfare maximization problem into the risk minimization
task in machine learning, and derive a fairness-aware scoring system with the
help of mixed integer programming. Lastly, several theoretical bounds are
derived for providing parameter selection suggestions. Our proposed framework
provides a suitable solution to address group fairness concerns in the
development of scoring systems. It enables policymakers to set and customize
their desired fairness requirements as well as other application-specific
constraints. We test the proposed algorithm with several empirical data sets.
Experimental evidence supports the effectiveness of the proposed scoring system
in achieving the optimal welfare of stakeholders and in balancing the needs for
interpretability, fairness, and efficiency.
