FairSearch: a tool for fairness in ranked search results

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Zehlike, Meike
• dc.contributor.author Sühr, Tom
• dc.contributor.author Castillo, Carlos
• dc.contributor.author Kitanovski, Ivan
• dc.date.accessioned 2021-04-15T06:30:42Z
• dc.date.available 2021-04-15T06:30:42Z
• dc.date.issued 2020
• dc.description Comunicació presentada al WWW'20: International World Wide Web Conference, celebrat del 20 al 24 d'abril de 2020 a Taipei, Taiwan.
• dc.description.abstract Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine business opportunities, education, access to benefits, and even social success. It is therefore of societal and ethical importance to ask whether search results can demote, marginalize, or exclude individuals of unprivileged groups or promote products with undesired features. In this paper we present FairSearch, the first fair open source search API to provide fairness notions in ranked search results. We implement two well-known algorithms from the literature, namely FA*IR (Zehlike et al., 9) and DELTR (Zehlike and Castillo, 10) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, a well-known search engine API based on Apache Lucene. The interfaces use the aforementioned Java libraries and enable search engine developers who wish to ensure fair search results of different styles to easily integrate DELTR and FA*IR into their existing Elasticsearch environment.en
• dc.description.sponsorship This project was realized with a research grant from Data Transparency Lab. Castillo is partially funded by La Caixa project LCF/PR/PR16/11110009. Zehlike is funded by the MPI-SWS.
• dc.format.mimetype application/pdf
• dc.identifier.citation Zehlike M, Sühr T, Castillo C, Kitanovski I. FairSearch: a tool for fairness in ranked search results. In: Seghrouchni AEF, Sukthankar G, Liu TY, van Steen M. WWW '20: Companion Proceedings of the Web Conference; 2020 Apr 20-24; Taipei, Taiwan. New York: Association for Computing Machinery; 2020. p. 172-75. DOI: 10.1145/3366424.3383534
• dc.identifier.doi http://dx.doi.org/10.1145/3366424.3383534
• dc.identifier.uri http://hdl.handle.net/10230/47122
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof Seghrouchni AEF, Sukthankar G, Liu TY, van Steen M. WWW '20: Companion Proceedings of the Web Conference; 2020 Apr 20-24; Taipei, Taiwan. New York: Association for Computing Machinery; 2020. p. 172-75
• dc.rights This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Rankingen
• dc.subject.keyword Algorithmic fairnessen
• dc.subject.keyword Disparate impacten
• dc.title FairSearch: a tool for fairness in ranked search resultsen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion