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.

本研究就机器学习在职业分类中潜在的性别歧视问题进行了大规模调研，分析了不同语义表征中包含显性性别表示（如名字和代词）对职业分类的影响，并量化了这些表示被 “擦除” 后残留的偏见，并描述了在显性性别表示缺失时出现的代理行为。同时，研究也表明，不同性别的真正正率差异与职业领域中现存的性别不平衡存在相关性，这可能加剧这些不平衡。