  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.
