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dc.contributor.author Tabibian, Behzad
dc.contributor.author Gómez, Vicenç
dc.contributor.author De, Abir
dc.contributor.author Schölkopf, Bernhard
dc.contributor.author Gomez-Rodriguez, Manuel
dc.date.accessioned 2021-05-06T07:50:16Z
dc.date.available 2021-05-06T07:50:16Z
dc.date.issued 2020
dc.identifier.citation Tabibian B, Gomez V, De A, Schölkopf B, Gomez Rodriguez M. On the design of consequential ranking algorithms. In: Peters J, Sontag D, editors. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI); 2020 Aug 3-6; Massachusetts, USA. Massachusetts: PMLR; 2020. p. 171-80.
dc.identifier.uri http://hdl.handle.net/10230/47337
dc.description Comunicació presentada al 36th Conference on Uncertainty in Artificial Intelligence (UAI), celebrat del 3 al 6 d'agost de 2020 de manera virtual.
dc.description.abstract Ranking models are typically designed to optimize some measure of immediate utility to the users. As a result, they have been unable to anticipate an increasing number of undesirable long-term consequences of their proposed rankings, from fueling the spread of misinformation and increasing polarization to degrading social discourse. Can we design ranking models that anticipate the consequences of their proposed rankings and are able to avoid the undesirable ones? In this paper, we first introduce a joint representation of rankings and user dynamics using Markov decision processes. Then, we show that this representation greatly simplifies the construction of consequential ranking models that trade off the immediate utility and the long-term welfare. In particular, we can obtain optimal consequential rankings by applying weighted sampling on the rankings provided by models that maximize measures of immediate utility. However, in practice, such a strategy may be inefficient and impractical, specially in high dimensional scenarios. To overcome this, we introduce an efficient gradient-based algorithm to learn parameterized consequential ranking models that effectively approximate optimal ones. We illustrate our methodology using synthetic and real data gathered from Reddit and show that our consequential rankings may mitigate the spread of misinformation and improve the civility of online discussions.
dc.description.sponsorship V. Gomez has received funding from “la Caixa” Foundation (ID 100010434), under the agreement LCF/PR/PR16/51110009 and by the Volkswagen Foundation, Courage project (95.566).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Proceedings of Machine Learning Research
dc.relation.ispartof Peters J, Sontag D, editors. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI); 2020 Aug 3-6; Massachusetts, USA. Massachusetts: PMLR; 2020. p. 171-80
dc.rights Creative Commons copyright license in the article to the general public, in particular a Creative Commons Attribution 4.0 International License, which is incorporated herein by reference and is further specified at http://creativecommons.org/licenses/by/4.0/legalcode (human readable summary at http://creativecommons.org/licenses/by/4.0).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title On the design of consequential ranking algorithms
dc.type info:eu-repo/semantics/conferenceObject
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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