  There is a growing body of work that proposes methods for mitigating bias in
machine learning systems. These methods typically rely on access to protected
attributes such as race, gender, or age. However, this raises two significant
challenges: (1) protected attributes may not be available or it may not be
legal to use them, and (2) it is often desirable to simultaneously consider
multiple protected attributes, as well as their intersections. In the context
of mitigating bias in occupation classification, we propose a method for
discouraging correlation between the predicted probability of an individual's
true occupation and a word embedding of their name. This method leverages the
societal biases that are encoded in word embeddings, eliminating the need for
access to protected attributes. Crucially, it only requires access to
individuals' names at training time and not at deployment time. We evaluate two
variations of our proposed method using a large-scale dataset of online
biographies. We find that both variations simultaneously reduce race and gender
biases, with almost no reduction in the classifier's overall true positive
rate.
