Gendered Language in Resumes

Anonymous

Abstract: Despite growing concerns around gender bias in NLP models used in algorithmic hiring, there is little empirical work studying the extent and nature of gendered language in resumes.Using a corpus of 709k resumes from IT firms, we train a series of models to classify the gender of the applicant, thereby measuring the extent of gendered information encoded in resumes.We also investigate whether it is possible to obfuscate gender from resumes by removing gender identifiers, removing gender sub-space in embedding models, etc.We find that there is a significant amount of gendered information in resumes even after obfuscation.A simple Tf-Idf model can learn to classify gender with AUROC=0.75, and more sophisticated transformer-based models achieve AUROC=0.8.We further find that gender predictive values have little correlation with gender direction of embeddings -- meaning that, what is predictive of gender is not necessarily ``gendered'' in the masculine/feminine sense.We discuss the implications of these findings in the algorithmic hiring context.