Context-Aware Discrimination Detection in Job Vacancies using Computational Language Models

TL;DR

This paper develops machine learning models to improve the detection of explicit gender discrimination in job ads by understanding context, leading to higher precision and uncovering new discriminatory patterns.

Contribution

It introduces a dataset and evaluates language models for context-aware discrimination detection, enhancing accuracy over existing keyword-based methods.

Findings

High precision detection of explicit gender discrimination

Effective identification of unforeseen discriminatory terms

Potential to reduce discriminatory job advertisements

Abstract

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.