  We investigate whether it is feasible to remove gendered information from
resumes to mitigate potential bias in algorithmic resume screening. Using a
corpus of 709k resumes from IT firms, we first train a series of models to
classify the self-reported gender of the applicant, thereby measuring the
extent and nature of gendered information encoded in resumes. We then conduct a
series of gender obfuscation experiments, where we iteratively remove gendered
information from resumes. Finally, we train a resume screening algorithm and
investigate the trade-off between gender obfuscation and screening algorithm
performance. Results show: (1) There is a significant amount of gendered
information in resumes. (2) Lexicon-based gender obfuscation method (i.e.
removing tokens that are predictive of gender) can reduce the amount of
gendered information to a large extent. However, after a certain point, the
performance of the resume screening algorithm starts suffering. (3)
General-purpose gender debiasing methods for NLP models such as removing gender
subspace from embeddings are not effective in obfuscating gender.
