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Individually-Fair Auctions for Multi-Slot Sponsored Search

Authors Shuchi Chawla, Rojin Rezvan, Nathaniel Sauerberg

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

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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].

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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

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.

References

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