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We conduct a study of hiring bias on a simulation platform where we askAmazon MTurk participants to make hiring decisions for a mathematicallyintensive task. Our findings suggest hiring biases against Black workers andless attractive workers and preferences towards Asian workers female workersand more attractive workers. We also show that certain UI designs includingprovision of candidates information at the individual level and reducing thenumber of choices can significantly reduce discrimination. However provision ofcandidates information at the subgroup level can increase discrimination. Theresults have practical implications for designing better online freelancemarketplaces.

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