Variances in ad impression outcomes across demographic groups are increasingly considered to be potentially indicative of algorithmic bias in personalized ads systems. While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems. VRS seeks to achieve a distribution of impressions with respect to selected protected class (PC) attributes that more closely aligns the demographics of an ad's eligible audience (a function of advertiser targeting criteria) with the audience who sees that ad, in a privacy-preserving manner. We first define metrics to quantify fairness gaps in terms of ad impression variances with respect to PC attributes including gender and estimated race. We then present the VRS for re-ranking ads in an impression variance-aware manner. We evaluate VRS via extensive simulations over different parameter choices and study the effect of the VRS on the chosen fairness metric. We finally present online A/B testing results from applying VRS to Meta's ads systems, concluding with a discussion of future work. We have deployed the VRS to all users in the US for housing ads, resulting in significant improvement in our fairness metric. VRS is the first large-scale deployed framework for pursuing fairness for multiple PC attributes in online advertising.

翻译：不同人口群体在广告曝光结果上的差异，日益被视为个性化广告系统可能存在算法偏差的潜在指标。尽管针对个性化系统存在多种公平性定义，我们提出了一种名为方差归约系统（VRS）的框架，旨在实现Meta广告系统中更公平的投放结果。VRS以隐私保护的方式，针对所选受保护类别（PC）属性，使广告曝光分布更接近其合格受众（由广告商定向条件决定）的人口统计特征。我们首先定义了用于量化公平性差距的指标，这些指标衡量广告在性别和估算种族等PC属性上的曝光方差。随后提出VRS，以曝光方差感知的方式对广告进行重排序。我们通过不同参数选择下的广泛模拟评估VRS，并研究其对所选公平性指标的影响。最后展示将VRS应用于Meta广告系统的在线A/B测试结果，并讨论未来工作方向。目前，VRS已在美国全量住房广告用户中部署，显著提升了公平性指标。VRS是首个大规模部署的、针对在线广告中多种PC属性实现公平性的框架。