Document Open Access Logo

Individually-Fair Auctions for Multi-Slot Sponsored Search

Authors Shuchi Chawla, Rojin Rezvan, Nathaniel Sauerberg

PDF
Thumbnail PDF

File

LIPIcs.FORC.2022.4.pdf
  • Filesize: 0.75 MB
  • 22 pages

Document Identifiers

Author Details

Shuchi Chawla
  • Department of Computer Science, University of Texas at Austin, TX, USA
Rojin Rezvan
  • Department of Computer Science, University of Texas at Austin, TX, USA
Nathaniel Sauerberg
  • Department of Computer Science, University of Texas at Austin, TX, USA

Funding

This work was supported in part by NSF Award CCF-2008006.

Cite AsGet BibTex

Shuchi Chawla, Rojin Rezvan, and Nathaniel Sauerberg. Individually-Fair Auctions for Multi-Slot Sponsored Search. In 3rd Symposium on Foundations of Responsible Computing (FORC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 218, pp. 4:1-4:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.FORC.2022.4

Abstract

We design fair sponsored search auctions that achieve a near-optimal tradeoff between fairness and quality. Our work builds upon the model and auction design of Chawla and Jagadeesan [Chawla and Jagadeesan, 2022], who considered the special case of a single slot. We consider sponsored search settings with multiple slots and the standard model of click through rates that are multiplicatively separable into an advertiser-specific component and a slot-specific component. When similar users have similar advertiser-specific click through rates, our auctions achieve the same near-optimal tradeoff between fairness and quality as in [Chawla and Jagadeesan, 2022]. When similar users can have different advertiser-specific preferences, we show that a preference-based fairness guarantee holds. Finally, we provide a computationally efficient algorithm for computing payments for our auctions as well as those in previous work, resolving another open direction from [Chawla and Jagadeesan, 2022].

Subject Classification

ACM Subject Classification
  • Theory of computation → Algorithmic mechanism design
Keywords
  • algorithmic fairness
  • advertising auctions
  • and individual fairness

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Related Versions

References

  1. Gagan Aggarwal, Ashish Goel, and Rajeev Motwani. Truthful auctions for pricing search keywords. In Proceedings of the 7th ACM Conference on Electronic Commerce, EC '06, pages 1-7, New York, NY, USA, 2006. Association for Computing Machinery. URL: https://doi.org/10.1145/1134707.1134708.
  2. Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. Discrimination through optimization: How facebook’s ad delivery can lead to biased outcomes. Proc. ACM Hum.-Comput. Interact., 3(CSCW), November 2019. URL: https://doi.org/10.1145/3359301.
  3. Andrea Celli, Riccardo Colini-Baldeschi, and Stefano Leonardi. Learning fair equilibria in sponsored search auctions. arXiv preprint, 2021. URL: http://arxiv.org/abs/2107.08271.
  4. Shuchi Chawla, Christina Ilvento, and Meena Jagadeesan. Multi-category fairness in sponsored search auctions. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20, pages 348-358, New York, NY, USA, 2020. Association for Computing Machinery. Google Scholar
  5. Shuchi Chawla and Meena Jagadeesan. Individual fairness in advertising auctions through inverse proportionality. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2022. Google Scholar
  6. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS '12, pages 214-226, New York, NY, USA, 2012. Association for Computing Machinery. URL: https://doi.org/10.1145/2090236.2090255.
  7. Cynthia Dwork and Christina Ilvento. Fairness under composition. In 10th Innovations in Theoretical Computer Science Conference, ITCS 2019, January 10-12, 2019, San Diego, California, USA, pages 33:1-33:20, 2019. Google Scholar
  8. Meryem Essaidi and S Matthew Weinberg. On symmetries in multi-dimensional mechanism design. In International Conference on Web and Internet Economics, pages 59-75. Springer, 2021. Google Scholar
  9. Lodewijk Gelauff, Ashish Goel, Kamesh Munagala, and Sravya Yandamuri. Advertising for demographically fair outcomes. arXiv preprint, 2020. URL: http://arxiv.org/abs/2006.03983.
  10. Julia Angwin and Terry Perris. Facebook lets advertisers exclude users by race, 2016. URL: https://www.propublica.org/article/facebook-lets-advertisers-exclude-users-by-race.
  11. Julia Angwin, Noam Scheiber, and Ariana Tobin. Facebook job ads raise concerns about age discrimination, 2017. URL: https://www.nytimes.com/2017/12/20/business/facebook-job-ads.html.
  12. Katie Benner, Glenn Thrush, and Mike Isaac. Facebook engages in housing discrimination with its ad practices, u.s. says, 2019. URL: https://www.nytimes.com/2019/03/28/us/politics/facebook-housing-discrimination.html.
  13. Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, and Gal Yona. Preference-informed fairness. In Proceedings of the 11th Innovations in Theoretical Computer Science, ITCS 2020, Seattle, Washington, USA, January 12-14, 2020, page to appear, 2020. Google Scholar
  14. A Lambrecht and C E Tucker. Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads. Management Science, 65(7):2966-2981, July 2019. (C) 2019 INFORMS This manuscript has been accepted for publication in Management Science. The version of record can be found at https://doi.org/10.1287/mnsc.2018.3093. URL: https://lbsresearch.london.edu/id/eprint/967/.
  15. 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, FAT* '20, pages 337-347, New York, NY, USA, 2020. Association for Computing Machinery. URL: https://doi.org/10.1145/3351095.3375783.
  16. Alison Watts. Fairness and efficiency in online advertising mechanisms. Games, 12(2):36, 2021. Google Scholar
  17. Di Yuan, Manmohan Aseri, and Tridas Mukhopadhyay. Is fair advertising good for platforms? Available at SSRN 3913739, 2021. Google Scholar
  18. Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, and Adrian Weller. From parity to preference-based notions of fairness in classification. Advances in Neural Information Processing Systems, 30, 2017. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023 Schloss Dagstuhl - LZI GmbH Imprint Privacy Contact