Hu, S;
Al-Ani, JA;
Hughes, KD;
Denier, N;
Konnikov, A;
Ding, L;
Xie, J;
... Dai, H; + view all
(2022)
Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation.
Frontiers in Big Data
, 5
, Article 805713. 10.3389/fdata.2022.805713.
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Abstract
Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications.
| Type: | Article |
|---|---|
| Title: | Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fdata.2022.805713 |
| Publisher version: | https://doi.org/10.3389/fdata.2022.805713 |
| Language: | English |
| Additional information: | © 2022 Hu, Al-Ani, Hughes, Denier, Konnikov, Ding, Xie, Hu, Tarafdar, Jiang, Kong and Dai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Bias evaluation, bias mitigation, constrained sampling, gender bias, importance sampling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210752 |
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