License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2023.3
URN: urn:nbn:de:0030-drops-179245
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Navon, Inbal Livni ; Peale, Charlotte ; Reingold, Omer ; Shen, Judy Hanwen

Bidding Strategies for Proportional Representation in Advertisement Campaigns

LIPIcs-FORC-2023-3.pdf (0.8 MB)


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.

BibTeX - Entry

  author =	{Navon, Inbal Livni and Peale, Charlotte and Reingold, Omer and Shen, Judy Hanwen},
  title =	{{Bidding Strategies for Proportional Representation in Advertisement Campaigns}},
  booktitle =	{4th Symposium on Foundations of Responsible Computing (FORC 2023)},
  pages =	{3:1--3:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-272-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{256},
  editor =	{Talwar, Kunal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-179245},
  doi =		{10.4230/LIPIcs.FORC.2023.3},
  annote =	{Keywords: Algorithmic fairness, diversity, advertisement auctions}

Keywords: Algorithmic fairness, diversity, advertisement auctions
Collection: 4th Symposium on Foundations of Responsible Computing (FORC 2023)
Issue Date: 2023
Date of publication: 04.06.2023
Supplementary Material: Software (Source Code): archived at:

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