种族、性别与美貌：信息提供对网络招聘偏见的影响
Race, Gender and Beauty: The Effect of Information Provision on Online Hiring Biases
摘要：
我们在一个模拟平台上进行了一项关于招聘偏见的研究，我们要求亚马逊MTurk参与者为一项数学密集型任务做出招聘决定。我们的研究结果表明，对黑人工人和不太有吸引力的工人的雇用偏见和对亚洲工人，女性工人和更有吸引力的工人的偏好。我们还表明，某些UI设计，包括提供候选人的个人信息，减少选择的数量，可以显着减少歧视。然而，在分组一级提供候选人信息可能会增加歧视。研究结果对设计更好的在线自由职业者市场具有实际意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
