Computer Science > Machine Learning
[Submitted on 21 Sep 2021 (v1), last revised 22 Nov 2022 (this version, v4)]
Title:Toward a Fairness-Aware Scoring System for Algorithmic Decision-Making
View PDFAbstract: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.
Submission history
From: Yi Yang [view email][v1] Tue, 21 Sep 2021 09:46:35 UTC (790 KB)
[v2] Mon, 27 Sep 2021 14:19:13 UTC (1,302 KB)
[v3] Sun, 9 Jan 2022 05:12:34 UTC (2,597 KB)
[v4] Tue, 22 Nov 2022 12:39:37 UTC (4,108 KB)
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