  (Bolukbasi et al., 2016) demonstrated that pretrained word embeddings can
inherit gender bias from the data they were trained on. We investigate how this
bias affects downstream classification tasks, using the case study of
occupation classification (De-Arteaga et al.,2019). We show that traditional
techniques for debiasing embeddings can actually worsen the bias of the
downstream classifier by providing a less noisy channel for communicating
gender information. With a relatively minor adjustment, however, we show how
these same techniques can be used to simultaneously reduce bias and maintain
high classification accuracy.
