评估金融交易模型的公平性，助力风控系统避免歧视性决策。
Measuring Fairness in Financial Transaction Machine Learning Models
- 基于真实金融数据构建公平性评估框架
- 识别跨区域与行业消费模式中的潜在偏差
- 适合金融科技、AI伦理研究者参考
万事达卡作为全球金融服务领导者，开发并部署机器学习模型以优化信用卡使用并预防客户流失。这些模型利用聚合且匿名化的信用卡使用模式（包括跨境交易和行业特定消费）来定制银行产品并最大化收入机会。万事达卡建立了基于其数据与技术责任原则的AI治理计划，用于评估自研及采购AI在有效性、公平性和透明度方面的表现。作为该计划的一部分，万事达卡通过数据研究小组项目向图灵研究所寻求支持，以更好地评估复杂AI/ML模型中的公平性。该研究挑战在于定义、测量和缓解预测中可能存在的不公平问题，这因公平性的多重解释、研究文献缺口以及机器学习运维难题而变得复杂。
原文摘要 · Abstract (English)
Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive models. These models use aggregated and anonymized card usage patterns, including cross-border transactions and industry-specific spending, to tailor bank offerings and maximize revenue opportunities. Mastercard has established an AI Governance program, based on its Data and Tech Responsibility Principles, to evaluate any built and bought AI for efficacy, fairness, and transparency. As part of this effort, Mastercard has sought expertise from the Turing Institute through a Data Study Group to better assess fairness in more complex AI/ML models. The Data Study Group challenge lies in defining, measuring, and mitigating fairness in these predictions, which can be complex due to the various interpretations of fairness, gaps in the research literature, and ML-operations challenges.
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