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

• 成果类型: Article; Early Access
• 署名作者: Yang, Yi; Wu, Ying; Chang, Xiangyu; Li, Mei; Tan, Yong
• 署名单位: Arizona State University; Arizona State University-Tempe; Xi'an Jiaotong University; University of Oklahoma System; University of Oklahoma - Norman; University of Washington; University of Washington Seattle
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478251318918
• 发表日期: 2025
• 关键词: fairness; Machine Learning; Scoring System; Mixed-Integer Programming

摘要

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 (ML) algorithms in the construction of scoring systems heightens this concern. This article proposes 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 ML, 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 developing 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.