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Race, Gender and Beauty: The Effect of Information Provision on Online Hiring Biases

dc.contributor.authorLeung, Weiwen
dc.contributor.authorZhang, Zheng
dc.contributor.authorJibuti, Daviti
dc.contributor.authorZhao, Jinhao
dc.contributor.authorKlein, Maximillian
dc.contributor.authorPierce, Casey
dc.contributor.authorRobert, Lionel + "Jr"
dc.contributor.authorZhu, Haiyi
dc.date.accessioned2020-01-16T11:22:57Z
dc.date.available2020-01-16T11:22:57Z
dc.date.issued2020-01-13
dc.identifier.citationLeung, W., Zhang, Z., Jibuti, D., Zhao, J., Klein, M., Pierce, C, Robert, L.P., Zhu, H., (2020). Race, Gender and Beauty: The Effect of Information Provision Affects Online Hiring Biases, Proceedings of the 38rd ACM Conference on Human Factors in Computing Systems (CHI 2020), April 25-30, 2020, Honolulu, Hawaii, USA.en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/153289
dc.description.abstractWe conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that certain UI designs, including provision of candidates’ information at the individual level and reducing the number of choices, can significantly reduce discrimination. However, provision of candidate’s information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.en_US
dc.description.sponsorshipNational Science Foundation Grant IIS-2001851en_US
dc.description.sponsorshipNational Science Foundation Grant IIS-2000782en_US
dc.description.sponsorshipNational Science Foundation Grant IIS-1939606en_US
dc.language.isoen_USen_US
dc.publisherCHI 2020en_US
dc.subjecthiring biasen_US
dc.subjectracial biasen_US
dc.subjectgender biasen_US
dc.subjectbeauty biasen_US
dc.subjecthiring discriminationen_US
dc.subjectracial discriminationen_US
dc.subjectgender discriminationen_US
dc.subjecthuman resourcesen_US
dc.subjectworkforce managementen_US
dc.subjectfreelance marketplacesen_US
dc.subjectsharing economyen_US
dc.subjectplatform worken_US
dc.subjectUX designen_US
dc.subjectUser Interfaceen_US
dc.subjectHuman Computer Interactionen_US
dc.subjectOnline Hiringen_US
dc.subjectInformation Provisionen_US
dc.subjectAmazon MTurken_US
dc.subjectonline platformsen_US
dc.subjectuser interface designen_US
dc.subjectgig marketen_US
dc.subjectgig worken_US
dc.subjectgig economyen_US
dc.subjectjob discriminationen_US
dc.subjectworkplace discriminationen_US
dc.subjecthiring decisionsen_US
dc.subjectjob candidatesen_US
dc.subjectemployment biasen_US
dc.subjectemployment discriminationen_US
dc.titleRace, Gender and Beauty: The Effect of Information Provision on Online Hiring Biasesen_US
dc.typeConference Paperen_US
dc.subject.hlbsecondlevelInformation and Library Science
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumInformation, School ofen_US
dc.contributor.affiliationotherUniversity of Rochesteren_US
dc.contributor.affiliationotherCERGE-EIen_US
dc.contributor.affiliationotherTsinghua Universityen_US
dc.contributor.affiliationotherUniversity of Minnesotaen_US
dc.contributor.affiliationotherCarnegie Mellon Universityen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153289/1/Leung et al. 2020.pdf
dc.identifier.doihttps://doi.org/10.1145/3313831.3376874
dc.identifier.sourceProceedings of the 38rd ACM Conference on Human Factors in Computing Systemsen_US
dc.identifier.orcid0000-0002-1410-2601en_US
dc.description.filedescriptionDescription of Leung et al. 2020.pdf : Mainfile
dc.identifier.name-orcidRobert, Lionel P.; 0000-0002-1410-2601en_US
dc.owningcollnameInformation, School of (SI)


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