Fairness Incentives for Myopic Agents

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dc.citation.firstpage369en_US
dc.citation.journalTitleProceedings of the 2017 ACM Conference on Economics and Computationen_US
dc.citation.lastpage386en_US
dc.contributor.authorKannan, Sampathen_US
dc.contributor.authorKearns, Michaelen_US
dc.contributor.authorMorgenstern, Jamieen_US
dc.contributor.authorPai, Malleshen_US
dc.contributor.authorRoth, Aaronen_US
dc.contributor.authorVohra, Rakeshen_US
dc.contributor.authorWu, Zhiwei Stevenen_US
dc.date.accessioned2017-11-14T18:08:24Zen_US
dc.date.available2017-11-14T18:08:24Zen_US
dc.date.issued2017en_US
dc.description.abstractWe 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 qualifie 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 setting with k arms, ~{O}(\sqrtk3T), and for the d-dimensional linear contextual setting ~{O}(d\sqrtk3T)). If the principal has much more limited information (as might often 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.en_US
dc.identifier.citationKannan, Sampath, Kearns, Michael, Morgenstern, Jamie, et al.. "Fairness Incentives for Myopic Agents." <i>Proceedings of the 2017 ACM Conference on Economics and Computation,</i> (2017) ACM: 369-386. https://doi.org/10.1145/3033274.3085154.en_US
dc.identifier.digitalpaper-ecen_US
dc.identifier.doihttps://doi.org/10.1145/3033274.3085154en_US
dc.identifier.urihttps://hdl.handle.net/1911/98811en_US
dc.language.isoengen_US
dc.publisherACMen_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by ACM.en_US
dc.titleFairness Incentives for Myopic Agentsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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