  The goal of this work is to help mitigate the already existing gender wage
gap by supplying unbiased job recommendations based on resumes from job
seekers. We employ a generative adversarial network to remove gender bias from
word2vec representations of 12M job vacancy texts and 900k resumes. Our results
show that representations created from recruitment texts contain algorithmic
bias and that this bias results in real-world consequences for recommendation
systems. Without controlling for bias, women are recommended jobs with
significantly lower salary in our data. With adversarially fair
representations, this wage gap disappears, meaning that our debiased job
recommendations reduce wage discrimination. We conclude that adversarial
debiasing of word representations can increase real-world fairness of systems
and thus may be part of the solution for creating fairness-aware recommendation
systems.
