在保护隐私的前提下，实现对美国领英用户种族/族裔的公平性评估。
Productionized Fairness Measurement Under Privacy Constraints
- 融合隐私技术与双源数据，实现可信赖的种族/族裔估计。
- 支持候选人端与浏览端的公平性测量，保障数据安全。
- 为机构提供可复用的隐私保护评估框架，适合合规需求强的场景。
基于细分评估的公平性度量常依赖受法律或文化限制的种族/族裔信息。本文提出隐私保护的概率性种族/族裔估计（PPRE）方法，用于在美国领英用户中以隐私保护方式实现种族/族裔相关的公平性评估。PPRE 在两个种族/族裔数据源（贝叶斯改进姓氏地理编码估算器与稀疏自报黄金调查集）基础上，集成安全多方计算、差分隐私和加法同态加密等隐私技术，构建支持候选人端与观看端公平性度量的解决方案。本文详细说明其隐私保证，并展示其在真实场景中的应用。最后，提出一个可迁移的框架，供希望部署类似隐私保护度量基础设施的机构参考。
原文摘要 · Abstract (English)
Fairness measurements in the form of disaggregated evaluations often rely on demographic signals that are legally constrained or culturally sensitive. Race and ethnicity signals are among the more difficult signals to curate and use for this task. This paper presents Privacy-Preserving Probabilistic Race/Ethnicity Estimation (PPRE) as a method for enabling fairness measurements with respect to race/ethnicity for U.S.\ LinkedIn members in a privacy-preserving manner. PPRE applies privacy technologies (specifically: secure two-party computation, differential privacy, and additive homomorphic encryption) on top of two race/ethnicity demographic signal sources (the Bayesian Improved Surname Geocoding estimator and a sparse golden survey set of self-reported demographics) to power a fairness measurement solution with respect to US-based race/ethnicity demographics. We detail its privacy guarantees and demonstrate its application on candidate- and viewer-side fairness measurements. We close with a transferable framework for institutions seeking to implement similar privacy-preserving measurement infrastructure.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。