  Discriminatory job vacancies are disapproved worldwide, but remain
persistent. Discrimination in job vacancies can be explicit by directly
referring to demographic memberships of candidates. More implicit forms of
discrimination are also present that may not always be illegal but still
influence the diversity of applicants. Explicit written discrimination is still
present in numerous job vacancies, as was recently observed in the Netherlands.
Current efforts for the detection of explicit discrimination concern the
identification of job vacancies containing potentially discriminating terms
such as "young" or "male". However, automatic detection is inefficient due to
low precision: e.g. "we are a young company" or "working with mostly male
patients" are phrases that contain explicit terms, while the context shows that
these do not reflect discriminatory content.
  In this paper, we show how machine learning based computational language
models can raise precision in the detection of explicit discrimination by
identifying when the potentially discriminating terms are used in a
discriminatory context. We focus on gender discrimination, which indeed suffers
from low precision when filtering explicit terms. First, we created a data set
for gender discrimination in job vacancies. Second, we investigated a variety
of computational language models for discriminatory context detection. Third,
we evaluated the capability of these models to detect unforeseen discriminating
terms in context. The results show that machine learning based methods can
detect explicit gender discrimination with high precision and help in finding
new forms of discrimination. Accordingly, the proposed methods can
substantially increase the effectiveness of detecting job vacancies which are
highly suspected to be discriminatory. In turn, this may lower the
discrimination experienced at the start of the recruitment process.
