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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 AsGet 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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Supplementary Materials

References

  1. Gagan Aggarwal, Ashwinkumar Badanidiyuru, and Aranyak Mehta. Autobidding with constraints. In International Conference on Web and Internet Economics, pages 17-30. Springer, 2019. Google Scholar
  2. Sanjeev Arora, Elad Hazan, and Satyen Kale. The multiplicative weights update method: a meta-algorithm and applications. Theory of computing, 8(1):121-164, 2012. Google Scholar
  3. Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, and Jon Schneider. Contextual bandits with cross-learning. Advances in Neural Information Processing Systems, 32, 2019. Google Scholar
  4. Santiago R Balseiro and Yonatan Gur. Learning in repeated auctions with budgets: Regret minimization and equilibrium. Management Science, 65(9):3952-3968, 2019. Google Scholar
  5. Maya A Beasley and Mary J Fischer. Why they leave: The impact of stereotype threat on the attrition of women and minorities from science, math and engineering majors. Social Psychology of Education, 15:427-448, 2012. Google Scholar
  6. Christian Borgs, Jennifer Chayes, Nicole Immorlica, Kamal Jain, Omid Etesami, and Mohammad Mahdian. Dynamics of bid optimization in online advertisement auctions. In Proceedings of the 16th international conference on World Wide Web, pages 531-540, 2007. Google Scholar
  7. Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Giulia Romano, and Nicola Gatti. A unifying framework for online optimization with long-term constraints. arXiv preprint, 2022. URL: https://arxiv.org/abs/2209.07454.
  8. Elisa Celis, Anay Mehrotra, and Nisheeth Vishnoi. Toward controlling discrimination in online ad auctions. In International Conference on Machine Learning, pages 4456-4465. PMLR, 2019. Google Scholar
  9. Andrea Celli, Riccardo Colini-Baldeschi, Christian Kroer, and Eric Sodomka. The parity ray regularizer for pacing in auction markets. In Proceedings of the ACM Web Conference 2022, pages 162-172, 2022. Google Scholar
  10. Shuchi Chawla, Christina Ilvento, and Meena Jagadeesan. Multi-category fairness in sponsored search auctions. arXiv preprint, 2019. URL: https://arxiv.org/abs/1906.08732.
  11. Shuchi Chawla and Meena Jagadeesan. Individual Fairness in Advertising Auctions Through Inverse Proportionality. In Mark Braverman, editor, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022), volume 215 of Leibniz International Proceedings in Informatics (LIPIcs), pages 42:1-42:21, Dagstuhl, Germany, 2022. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/LIPIcs.ITCS.2022.42.
  12. Shuchi Chawla, Rojin Rezvan, and Nathaniel Sauerberg. Individually-fair auctions for multi-slot sponsored search. arXiv preprint, 2022. URL: https://arxiv.org/abs/2204.04136.
  13. Vincent Conitzer, Christian Kroer, Eric Sodomka, and Nicolas E. Stier-Moses. Multiplicative pacing equilibria in auction markets. Oper. Res., 70(2):963-989, March 2022. URL: https://doi.org/10.1287/opre.2021.2167.
  14. Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. Retiring adult: New datasets for fair machine learning. Advances in Neural Information Processing Systems, 34, 2021. Google Scholar
  15. Cynthia Dwork and Christina Ilvento. Fairness under composition. arXiv preprint, 2018. URL: https://arxiv.org/abs/1806.06122.
  16. Zhe Feng, Chara Podimata, and Vasilis Syrgkanis. Learning to bid without knowing your value. In Proceedings of the 2018 ACM Conference on Economics and Computation, EC '18, pages 505-522, New York, NY, USA, 2018. Association for Computing Machinery. URL: https://doi.org/10.1145/3219166.3219208.
  17. Google. Faqs about adsense moving to a first-price auction. URL: https://support.google.com/adsense/answer/10858748?hl=en.
  18. Yanjun Han, Zhengyuan Zhou, Aaron Flores, Erik Ordentlich, and Tsachy Weissman. Learning to bid optimally and efficiently in adversarial first-price auctions. arXiv preprint, 2020. URL: https://arxiv.org/abs/2007.04568.
  19. Yanjun Han, Zhengyuan Zhou, and Tsachy Weissman. Optimal no-regret learning in repeated first-price auctions. arXiv preprint, 2020. URL: https://arxiv.org/abs/2003.09795.
  20. Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J Curry, Samuel Dooley, Ping-yeh Chiang, Tom Goldstein, and John P Dickerson. Proportionnet: Balancing fairness and revenue for auction design with deep learning. arXiv preprint, 2020. URL: https://arxiv.org/abs/2010.06398.
  21. Anja Lambrecht and Catherine Tucker. Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads. Management science, 65(7):2966-2981, 2019. Google Scholar
  22. Brendan Lucier, Sarath Pattathil, Aleksandrs Slivkins, and Mengxiao Zhang. Autobidders with budget and roi constraints: Efficiency, regret, and pacing dynamics. arXiv preprint, 2023. URL: https://arxiv.org/abs/2301.13306.
  23. Milad Nasr and Michael Carl Tschantz. Bidding strategies with gender nondiscrimination constraints for online ad auctions. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 337-347, 2020. Google Scholar
  24. Ashwinkumar Badanidiyuru Varadaraja, Guru Prashanth Guruganesh, and Zhe Feng. Learning to bid in contextual first price auctions. In The Proceedings of the ACM Web Conference 2023, 2023. https://arxiv.org/abs/2109.03173.
  25. Jonathan Weed, Vianney Perchet, and Philippe Rigollet. Online learning in repeated auctions. In Vitaly Feldman, Alexander Rakhlin, and Ohad Shamir, editors, 29th Annual Conference on Learning Theory, volume 49 of Proceedings of Machine Learning Research, pages 1562-1583, Columbia University, New York, New York, USA, 23-26 June 2016. PMLR. URL: https://proceedings.mlr.press/v49/weed16.html.
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