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].
@InProceedings{chawla_et_al:LIPIcs.FORC.2022.4, author = {Chawla, Shuchi and Rezvan, Rojin and Sauerberg, Nathaniel}, title = {{Individually-Fair Auctions for Multi-Slot Sponsored Search}}, booktitle = {3rd Symposium on Foundations of Responsible Computing (FORC 2022)}, pages = {4:1--4:22}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-226-6}, ISSN = {1868-8969}, year = {2022}, volume = {218}, editor = {Celis, L. Elisa}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2022.4}, URN = {urn:nbn:de:0030-drops-165272}, doi = {10.4230/LIPIcs.FORC.2022.4}, annote = {Keywords: algorithmic fairness, advertising auctions, and individual fairness} }
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