  Online advertising on platforms such as Google or Facebook has become an
indispensable outreach tool, including for applications where it is desirable
to engage different demographics in an equitable fashion, such as hiring,
housing, civic processes, and public health outreach efforts. Somewhat
surprisingly, the existing online advertising ecosystem provides very little
support for advertising to (and recruiting) a demographically representative
cohort.
  We study the problem of advertising for demographic representativeness from
both an empirical and algorithmic perspective. In essence, we seek fairness in
the outcome or conversions generated by the advertising campaigns. We first
present detailed empirical findings from real-world experiments for recruiting
for civic processes, using which we show that methods using Facebook-inferred
features are too inaccurate for achieving equity in outcomes, while targeting
via custom audiences based on a list of registered voters segmented on known
attributes has much superior accuracy.
  This motivates us to consider the algorithmic question of optimally
segmenting the list of individuals with known attributes into a few custom
campaigns and allocating budgets to them so that we cost-effectively achieve
outcome parity with the population on the maximum possible number of
demographics. Under the assumption that a platform can reasonably enforce
proportionality in spend across demographics, we present efficient exact and
approximation algorithms for this problem. We present simulation results on our
datasets to show the efficacy of these algorithms in achieving demographic
parity.
