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

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dc.contributor.author Leung, Weiwen
dc.contributor.author Zhang, Zheng
dc.contributor.author Jibuti, Daviti
dc.contributor.author Zhao, Jinhao
dc.contributor.author Klein, Maximillian
dc.contributor.author Pierce, Casey
dc.contributor.author Robert, Lionel + "Jr"
dc.contributor.author Zhu, Haiyi
dc.date.accessioned 2020-01-16T11:22:57Z
dc.date.available 2020-01-16T11:22:57Z
dc.date.issued 2020-01-13
dc.identifier.citation Leung, 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.uri http://hdl.handle.net/2027.42/153289
dc.description.abstract We 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.sponsorship National Science Foundation Grant IIS-2001851 en_US
dc.description.sponsorship National Science Foundation Grant IIS-2000782 en_US
dc.description.sponsorship National Science Foundation Grant IIS-1939606 en_US
dc.language.iso en_US en_US
dc.publisher CHI 2020 en_US
dc.subject hiring bias en_US
dc.subject racial bias en_US
dc.subject gender bias en_US
dc.subject beauty bias en_US
dc.subject hiring discrimination en_US
dc.subject racial discrimination en_US
dc.subject gender discrimination en_US
dc.subject human resources en_US
dc.subject workforce management en_US
dc.subject freelance marketplaces en_US
dc.subject sharing economy en_US
dc.subject platform work en_US
dc.subject UX design en_US
dc.subject User Interface en_US
dc.subject Human Computer Interaction en_US
dc.subject Online Hiring en_US
dc.subject Information Provision en_US
dc.subject Amazon MTurk en_US
dc.subject online platforms en_US
dc.subject user interface design en_US
dc.subject gig market en_US
dc.subject gig work en_US
dc.subject gig economy en_US
dc.subject job discrimination en_US
dc.subject workplace discrimination en_US
dc.subject hiring decisions en_US
dc.subject job candidates en_US
dc.subject employment bias en_US
dc.subject employment discrimination en_US
dc.title Race, Gender and Beauty: The Effect of Information Provision on Online Hiring Biases en_US
dc.type Conference Paper en_US
dc.subject.hlbsecondlevel Information and Library Science
dc.subject.hlbtoplevel Social Sciences
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum Information, School of en_US
dc.contributor.affiliationother University of Rochester en_US
dc.contributor.affiliationother CERGE-EI en_US
dc.contributor.affiliationother Tsinghua University en_US
dc.contributor.affiliationother University of Minnesota en_US
dc.contributor.affiliationother Carnegie Mellon University en_US
dc.contributor.affiliationumcampus Ann Arbor en_US
dc.description.bitstreamurl https://deepblue.lib.umich.edu/bitstream/2027.42/153289/1/Leung et al. 2020.pdf
dc.identifier.doi https://doi.org/10.1145/3313831.3376874
dc.identifier.source Proceedings of the 38rd ACM Conference on Human Factors in Computing Systems en_US
dc.identifier.orcid 0000-0002-1410-2601 en_US
dc.description.filedescription Description of Leung et al. 2020.pdf : Mainfile
dc.identifier.name-orcid Robert, Lionel P.; 0000-0002-1410-2601 en_US
dc.owningcollname Information, School of (SI)
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