Fairness incentives for myopic agents

Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh Pai, Aaron Roth, Rakesh Vohra, Zhiwei Steven Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-Term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones [8]. We investigate whether it is possible to design inexpensive subsidy or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost o(T ) (for the classic se.ing with k arms, ∼O ( p k3T ), and for the d-dimensional linear contextual se.ing ∼O (d p k3T )). If the principal has much more limited information (as might open be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the k groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes.

Original languageEnglish (US)
Title of host publicationEC 2017 - Proceedings of the 2017 ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery, Inc
Pages369-386
Number of pages18
ISBN (Electronic)9781450345279
DOIs
StatePublished - Jun 20 2017
Externally publishedYes
Event18th ACM Conference on Economics and Computation, EC 2017 - Cambridge, United States
Duration: Jun 26 2017Jun 30 2017

Publication series

NameEC 2017 - Proceedings of the 2017 ACM Conference on Economics and Computation

Other

Other18th ACM Conference on Economics and Computation, EC 2017
CountryUnited States
CityCambridge
Period6/26/176/30/17

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    Kannan, S., Kearns, M., Morgenstern, J., Pai, M., Roth, A., Vohra, R., & Wu, Z. S. (2017). Fairness incentives for myopic agents. In EC 2017 - Proceedings of the 2017 ACM Conference on Economics and Computation (pp. 369-386). (EC 2017 - Proceedings of the 2017 ACM Conference on Economics and Computation). Association for Computing Machinery, Inc. https://doi.org/10.1145/3033274.3085154