用隐私保护方法估算用户族裔，助力算法公平性评估。
Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S
- 结合姓氏地理编码与稀疏调查数据，推断族裔信息。
- 通过安全多方计算和差分隐私保护成员隐私。
- 适合关注算法公平性且重视数据隐私的团队使用。
AI公平性评估通常需要按人口统计学群体细分系统表现，但种族、族裔等敏感属性难以获取。本文针对美国领英用户，提出隐私保护概率族裔估计（PPRE）方法。该方法融合贝叶斯改进姓氏地理编码（BISG）模型、稀疏自报族裔调查样本，以及安全两方计算和差分隐私等隐私增强技术，在保护成员隐私的前提下实现有意义的公平性测量。文中详述了方法细节与隐私保障机制，并展示若干测量实例。最后总结了扩展隐私保护公平性评估能力所面临的开放研究与工程挑战。
原文摘要 · Abstract (English)
AI fairness measurements, including tests for equal treatment, often take the form of disaggregated evaluations of AI systems. Such measurements are an important part of Responsible AI operations. These measurements compare system performance across demographic groups or sub-populations and typically require member-level demographic signals such as gender, race, ethnicity, and location. However, sensitive member-level demographic attributes like race and ethnicity can be challenging to obtain and use due to platform choices, legal constraints, and cultural norms. In this paper, we focus on the task of enabling AI fairness measurements on race/ethnicity for \emph{U.S. LinkedIn members} in a privacy-preserving manner. We present the Privacy-Preserving Probabilistic Race/Ethnicity Estimation (PPRE) method for performing this task. PPRE combines the Bayesian Improved Surname Geocoding (BISG) model, a sparse LinkedIn survey sample of self-reported demographics, and privacy-enhancing technologies like secure two-party computation and differential privacy to enable meaningful fairness measurements while preserving member privacy. We provide details of the PPRE method and its privacy guarantees. We then illustrate sample measurement operations. We conclude with a review of open research and engineering challenges for expanding our privacy-preserving fairness measurement capabilities.
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