Gender mobility in the labor market with skills-based matching models

TL;DR

This paper investigates gender segregation in skills-based job matching models, showing how language representations propagate gender bias and proposing evaluation methods to assess and mitigate this bias in labor market applications.

Contribution

It reveals gender segregation in language model-based skills representations and evaluates how different skills-based matching models propagate this bias using simulated data.

Findings

Gender segregation exists in language model-based skills representations.

Skills-based matching models propagate gender bias.

Evaluation methods can compare models on performance and bias risk.

Abstract

Skills-based matching promises mobility of workers between different sectors and occupations in the labor market. In this case, job seekers can look for jobs they do not yet have experience in, but for which they do have relevant skills. Currently, there are multiple occupations with a skewed gender distribution. For skills-based matching, it is unclear if and how a shift in the gender distribution, which we call gender mobility, between occupations will be effected. It is expected that the skills-based matching approach will likely be data-driven, including computational language models and supervised learning methods. This work, first, shows the presence of gender segregation in language model-based skills representation of occupations. Second, we assess the use of these representations in a potential application based on simulated data, and show that the gender segregation is propagated by various data-driven skills-based matching models.These models are based on different language representations (bag of words, word2vec, and BERT), and distance metrics (static and machine learning-based). Accordingly, we show how skills-based matching approaches can be evaluated and compared on matching performance as well as on the risk of gender segregation. Making the gender segregation bias of models more explicit can help in generating healthy trust in the use of these models in practice.