Degendering Resumes for Fair Algorithmic Resume Screening. (arXiv:2112.08910v3 [cs.CL] UPDATED)

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 …

arxiv resume resumes