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Bidding Strategies for Proportional Representation in Advertisement Campaigns

Authors Inbal Livni Navon , Charlotte Peale , Omer Reingold , Judy Hanwen Shen

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Author Details

Inbal Livni Navon
  • Stanford University, CA, USA
Charlotte Peale
  • Stanford University, CA, USA
Omer Reingold
  • Stanford University, CA, USA
Judy Hanwen Shen
  • Stanford University, CA, USA

Funding

  • Navon, Inbal Livni: Supported by the Sloan Foundation Grant 2020-13941 and the Zuckerman STEM Leadership Program.
  • Peale, Charlotte: Supported by the Simons Foundation Collaboration on the Theory of Algorithmic Fairness.
  • Reingold, Omer: Supported by the Simons Foundation Investigators Award 689988.
  • Shen, Judy Hanwen: Supported by the Simons Foundation Collaboration on the Theory of Algorithmic Fairness.

Cite As Get BibTex

Inbal Livni Navon, Charlotte Peale, Omer Reingold, and Judy Hanwen Shen. Bidding Strategies for Proportional Representation in Advertisement Campaigns. In 4th Symposium on Foundations of Responsible Computing (FORC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 256, pp. 3:1-3:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.FORC.2023.3

Abstract

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory and algorithms for application domains
Keywords
  • Algorithmic fairness
  • diversity
  • advertisement auctions

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References

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