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中算法偏差的潜在指标。尽管在个性化系统背景下存在多种公平性定义，但我们提出了一种名为方差缩减系统的框架，旨在实现Meta广告系统中更公平的曝光结果。VRS致力于在保护隐私的前提下，使选定受保护类属性的曝光分布更接近广告合格受众的人口统计特征（由广告主定向标准决定）与实际看到该广告的受众之间的匹配度。我们首先定义了量化公平性差距的指标，这些指标以广告曝光方差为衡量标准，涉及性别和估计种族等PC属性。随后，我们提出了VRS，以曝光方差感知的方式对广告进行重新排序。我们通过不同参数设置下的广泛模拟评估了VRS，并研究了VRS对所选公平性指标的影响。最后，我们展示了将VRS应用于Meta广告系统的在线A/B测试结果，并讨论了未来工作。VRS已部署至美国所有用户，用于住房广告，显著改善了我们的公平性指标。VRS是首个大规模部署的、用于在线广告中追求多PC属性公平性的框架。