  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.
