2020/01/16 by Weiwen Leung, Leung, Weiwen, Zheng Zhang +13 · 1 citation
Business, Management and Accounting · Social Sciences · #Computers and Society (cs.CY) #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Names, Identity, and Discrimination Research #Sharing Economy and Platforms #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2001.09753
openalex publication_date 2020/01/16 · openalex created_date 2020/06/05 · openalex updated_date 2026/07/28
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 candidates information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.