Toward a Fairness-Aware Scoring System for Algorithmic Decision-Making

TL;DR

This paper introduces a framework for creating fairness-aware scoring systems that balance efficiency and group fairness, using social welfare functions and machine learning techniques, with theoretical bounds and empirical validation.

Contribution

It presents a novel, general framework that incorporates fairness into scoring systems via social welfare optimization and risk minimization, with customizable fairness constraints.

Findings

The proposed system effectively balances fairness and efficiency.

The framework provides theoretical bounds for parameter choices.

Empirical tests show improved fairness without sacrificing interpretability.

Abstract

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.