Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation.
Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation.
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DOI：
10.3389/fdata.2022.805713
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发表时间：
2022
影响因子：
3.1
通讯作者：
Dai H
中科院分区：
文献类型：
--
作者：
Hu S;Al-Ani JA;Hughes KD;Denier N;Konnikov A;Ding L;Xie J;Hu Y;Tarafdar M;Jiang B;Kong L;Dai H
尽管过去几十年劳动力市场在性别平等方面取得了进展，但劳动力构成和劳动力市场结果中的性别隔离仍然存在。有证据表明，招聘广告可能表达性别偏好，这可能有选择地吸引潜在求职者申请特定职位，从而强化劳动力的性别构成和结果。从招聘广告中删除性别明确的词语并不能完全解决问题，因为某些隐性特征与男性更密切相关，例如雄心勃勃，而另一些隐性特征则与女性更密切相关，例如体贴。然而，并不总是能够找到这些特征的中性替代品，因此很难在不产生性别歧视的情况下寻找具有所需特征的候选人。现有算法主要侧重于检测招聘广告中是否存在性别偏见，而没有提供文本应如何（重新）措辞的解决方案。为了解决这个问题，我们提出了一种算法，可以评估输入文本中的性别偏见，并通过提供与原始输入密切相关的替代措辞来提供如何消除文本偏见的指导。我们提出的方法有望在人力资源流程中得到广泛应用，从招聘广告的开发到算法辅助筛选工作申请。
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.