Individual Fairness in Advertising Auctions Through Inverse Proportionality

Authors Shuchi Chawla, Meena Jagadeesan

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Shuchi Chawla
  • The University of Texas at Austin, TX, USA
Meena Jagadeesan
  • University of California, Berkeley, CA, USA

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Shuchi Chawla and Meena Jagadeesan. Individual Fairness in Advertising Auctions Through Inverse Proportionality. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 42:1-42:21, Schloss Dagstuhl – Leibniz-Zentrum fΓΌr Informatik (2022)


Recent empirical work demonstrates that online advertisement can exhibit bias in the delivery of ads across users even when all advertisers bid in a non-discriminatory manner. We study the design ad auctions that, given fair bids, are guaranteed to produce fair outcomes. Following the works of Dwork and Ilvento [2019] and Chawla et al. [2020], our goal is to design a truthful auction that satisfies "individual fairness" in its outcomes: informally speaking, users that are similar to each other should obtain similar allocations of ads. Within this framework we quantify the tradeoff between social welfare maximization and fairness. This work makes two conceptual contributions. First, we express the fairness constraint as a kind of stability condition: any two users that are assigned multiplicatively similar values by all the advertisers must receive additively similar allocations for each advertiser. This value stability constraint is expressed as a function that maps the multiplicative distance between value vectors to the maximum allowable 𝓁_{∞} distance between the corresponding allocations. Standard auctions do not satisfy this kind of value stability. Second, we introduce a new class of allocation algorithms called Inverse Proportional Allocation that achieve a near optimal tradeoff between fairness and social welfare for a broad and expressive class of value stability conditions. These allocation algorithms are truthful and prior-free, and achieve a constant factor approximation to the optimal (unconstrained) social welfare. In particular, the approximation ratio is independent of the number of advertisers in the system. In this respect, these allocation algorithms greatly surpass the guarantees achieved in previous work. We also extend our results to broader notions of fairness that we call subset fairness.

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ACM Subject Classification
  • Theory of computation β†’ Algorithmic mechanism design
  • Algorithmic fairness
  • advertising auctions


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  1. 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. PACMHCI, 3(CSCW):199:1-199:30, 2019. Google Scholar
  2. Julia Angwin and Terry Perris. Facebook lets advertisers exclude users by race. ProPublica, 2016. Google Scholar
  3. Julia Angwin, Noam Scheiber, and Ariana Tobin. Facebook job ads raise concerns about age discrimination. The New York Times in collaboration with ProPublica, 2017. Google Scholar
  4. Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, and Ariel D. Procaccia. Envy-free classification. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, pages 1238-1248, 2019. Google Scholar
  5. Ioannis Caragiannis and Alexandros A. Voudouris. Welfare guarantees for proportional allocations. Theory Comput. Syst., 59(4):581-599, 2016. Google Scholar
  6. L. Elisa Celis, Anay Mehrotra, and Nisheeth K. Vishnoi. Toward controlling discrimination in online ad auctions. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, pages 4456-4465, 2019. Google Scholar
  7. 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
  8. George Christodoulou, Alkmini Sgouritsa, and Bo Tang. On the efficiency of the proportional allocation mechanism for divisible resources. Theory Comput. Syst., 59(4):600-618, 2016. Google Scholar
  9. Amit Datta, Michael Carl Tschantz, and Anupam Datta. Automated experiments on ad privacy settings. PoPETs, 2015(1):92-112, 2015. Google Scholar
  10. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel. Fairness through awareness. In 3rd Innovations in Theoretical Computer Science, ITCS 2012, Cambridge, MA, USA, January 8-10, 2012, pages 214-226, 2012. Google Scholar
  11. 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
  12. Lodewijk Gelauff, Ashish Goel, Kamesh Munagala, and Sravya Yandamuri. Advertising for demographically fair outcomes. CoRR, abs/2006.03983, 2020. Google Scholar
  13. Lily Hu and Yiling Chen. Fair classification and social welfare. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20, pages 535-545, New York, NY, USA, 2020. Association for Computing Machinery. Google Scholar
  14. Ramesh Johari and John N. Tsitsiklis. Efficiency loss in a network resource allocation game. Math. Oper. Res., 29(3):407-435, 2004. Google Scholar
  15. 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
  16. Ava Kofman and Ariana Tobin. Facebook ads can still discriminate against women and older workers, despite a civil rights settlement. ProPublica, 2019. Google Scholar
  17. 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
  18. Frank McSherry and Kunal Talwar. Mechanism design via differential privacy. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2007), October 20-23, 2007, Providence, RI, USA, Proceedings, pages 94-103. IEEE Computer Society, 2007. Google Scholar
  19. 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. Google Scholar
  20. Latanya Sweeney. Discrimination in online ad delivery. Commun. ACM, 56(5):44-54, 2013. Google Scholar
  21. Yahoo. Yahoo. a1 - yahoo! search marketing advertising bidding data. URL:
  22. Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, and Adrian Weller. From parity to preference-based notions of fairness in classification. In Advances in Neural Information Processing Systems 30: NeurIPS 2017, 4-9 December 2017, Long Beach, CA, USA, pages 229-239, 2017. Google Scholar
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