  Many companies rely on advertising platforms such as Google, Facebook, or
Instagram to recruit a large and diverse applicant pool for job openings. Prior
works have shown that equitable bidding may not result in equitable outcomes
due to heterogeneous levels of competition for different types of individuals.
Suggestions have been made to address this problem via revisions to the
advertising platform. However, it may be challenging to convince platforms to
undergo a costly re-vamp of their system, and in addition it might not offer
the flexibility necessary to capture the many types of fairness notions and
other constraints that advertisers would like to ensure. Instead, we consider
alterations that make no change to the platform mechanism and instead change
the bidding strategies used by advertisers. We compare two natural fairness
objectives: one in which the advertisers must treat groups equally when bidding
in order to achieve a yield with group-parity guarantees, and another in which
the bids are not constrained and only the yield must satisfy parity
constraints. We show that requiring parity with respect to both bids and yield
can result in an arbitrarily large decrease in efficiency compared to requiring
equal yield proportions alone. We find that autobidding is a natural way to
realize this latter objective and show how existing work in this area can be
extended to provide efficient bidding strategies that provide high utility
while satisfying group parity constraints as well as deterministic and
randomized rounding techniques to uphold these guarantees. Finally, we
demonstrate the effectiveness of our proposed solutions on data adapted from a
real-world employment dataset.
