  We present a large-scale study of gender bias in occupation classification, a
task where the use of machine learning may lead to negative outcomes on
peoples' lives. We analyze the potential allocation harms that can result from
semantic representation bias. To do so, we study the impact on occupation
classification of including explicit gender indicators---such as first names
and pronouns---in different semantic representations of online biographies.
Additionally, we quantify the bias that remains when these indicators are
"scrubbed," and describe proxy behavior that occurs in the absence of explicit
gender indicators. As we demonstrate, differences in true positive rates
between genders are correlated with existing gender imbalances in occupations,
which may compound these imbalances.
