论文标题
种族，性别与美：信息提供对在线招聘偏见的影响
Race, Gender and Beauty: The Effect of Information Provision on Online Hiring Biases
论文作者
论文摘要
我们对模拟平台上的雇用偏见进行了研究，我们要求亚马逊MTURK参与者做出数学密集任务的招聘决定。我们的发现表明，雇用对黑人工人的偏见，对亚洲工人和更具吸引力的工人的吸引力和偏好。我们还表明，某些UI设计，包括在个人级别提供候选人信息以及减少选择次数可以大大减少歧视。但是，在亚组级别提供候选人信息可以增加歧视。结果对于设计更好的在线自由市场具有实际影响。
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.
