Departamento de Psicología, University of Deusto, Bilbao, Spain.
PLoS One. 2021 Dec 10;16(12):e0260409. doi: 10.1371/journal.pone.0260409. eCollection 2021.
Numerous field experiments based on the correspondence testing procedure have documented that gender bias influences personnel selection processes. Nowadays, algorithms and job platforms are used for personnel selection processes because of their supposed neutrality, efficiency, and costs savings. However, previous research has shown that algorithms can exhibit and even amplify gender bias. The present research aimed to explore a possible gender bias in automated-job alerts generated in InfoJobs, a popular job platform in Spain. Based on the correspondence testing procedure, we designed eight matched resumes in which we manipulated the gender of the candidate for two different professional sectors (female-dominated vs. male-dominated) and two different levels of age (24 vs. 38). We examined the 3,438 offers received. No significant differences were observed in the automated-job alerts received by female and male candidates as a function of occupation category, salary, and the number of long-term contracts included in the alerts. However, we found significant differences between the female-dominated and the male-dominated sectors in all the mentioned variables. Some limitations and implications of the study are discussed. The data and materials for this research are available at the Open Science Framework, https://osf.io/kptca/.
大量基于对应测试程序的现场实验已经证明，性别偏见会影响人员选拔过程。如今，由于算法和工作平台具有中立性、高效性和成本节约性，因此被用于人员选拔过程。然而，先前的研究表明，算法可能会表现出甚至放大性别偏见。本研究旨在探索西班牙流行的工作平台 InfoJobs 中生成的自动化工作提醒中是否存在性别偏见。我们基于对应测试程序，设计了 8 份匹配的简历，其中我们对候选人的性别进行了操纵，涉及两个不同的职业领域（女性主导与男性主导）和两个不同的年龄层次（24 岁与 38 岁）。我们检查了收到的 3438 份工作邀请。我们没有观察到女性和男性候选人收到的自动化工作提醒在职业类别、工资和提醒中包含的长期合同数量方面存在显著差异。然而，我们发现所有提到的变量在女性主导和男性主导的领域之间存在显著差异。讨论了该研究的一些局限性和影响。本研究的数据和材料可在开放科学框架上获得，网址为 https://osf.io/kptca/。
