A new approach towards ensuring gender inclusive SE job advertisements
A new approach towards ensuring gender inclusive SE job advertisements
复制标题
确保社会企业招聘广告性别包容的新方法
DOI：
10.1145/3510458.3513016
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Aniruddha, Gayatri
中科院分区：
文献类型：
--
作者：
Kanij, Tanjila;Grundy, John;McIntosh, Jennifer;Sarma, Anita;Aniruddha, Gayatri
或许，大多数软件工程师都是男性，因为软件工程 (SE) 职位的广告方式存在性别偏见。到目前为止，只有基于单词的检查工具可用于识别性别偏见（例如，“分析师”被认为是男性词）。然而，这种基于单词的分析最终将 SE 职位所需的技能识别为男性单词，因此还不够。在这项工作中，我们提出了一种更细致的机制，通过建立在 GenderMag 方法的基础上来检查 SE 招聘广告中的性别偏见，该方法已被证明在软件界面中的性别偏见检测方面是成功的。通过对 44 名软件从业者的调查，我们确定了男性和女性参与者存在差异的 16 个因素，并根据主题分析得出了 SE 求职者的三个角色方面。我们通过一项小型调查验证了这些方面，其中 SE 候选人与这些因素的描述相关。我们利用这些方面进行了一项试点研究，评估了四则 SE 招聘广告，并发现了其中两则与性别相关的偏见。软件工程 (SE) 劳动力以男性员工为主。这可能是因为SE招聘广告对男性存在性别偏见。通常使用基于单词的偏见检测工具来检查 SE 招聘广告是否存在潜在的性别偏见。然而，这些并没有考虑软件工程的特定词汇。例如，使用基于单词的工具将“分析师”一词视为男性，因此任何SE分析师职位都会被视为（错误地）偏向男性。因此，这种基于单词的工具不足以准确评估SE招聘广告中的性别偏见。我们基于 GenderMag 方法提出了一种更细致的机制来检查 SE 招聘广告中的性别偏见。事实证明，GenderMag 在软件界面中的性别偏见检测方面取得了成功。该方法确定了男性和女性软件用户不同的问题解决的五个维度（称为方面）。通过对软件从业者的调查，我们发现了男性和女性 SE 求职者的不同因素。我们将相似的因素分组在一起，得出对预测 SE 求职者的工作申请行为很重要的三个方面。我们与一些 SE 候选人验证了这些方面，并使用它们从这些方面开发“新”Tim 和 Abi 角色，并评估了四个 SE 招聘广告。我们在其中两个中发现了与性别相关的偏见。
A majority of software engineers are male, perhaps, because of the very way that software engineering (SE) roles are advertised is gender biased. Thus far, only word-based checking tools are available to identify gender biases (e.g., "analyst" is considered a masculine word). However, such word-based analyses end up identifying the skills required for SE job positions as masculine words, and therefore, not sufficient. In this work, we present a more nuanced mechanism to check for gender bias in SE job advertisements by building on the GenderMag method, which has proven to be successful in gender bias detection in software interfaces. From a survey of 44 software practitioners, we identified 16 factors where male and female participants differ and based on a thematic analysis we derived three SE job applicant persona facets. We verified the facets with a small survey where SE candidates related to the descriptions of those factors. We conducted a pilot study using these facets to evaluate four SE job advertisements and identified gender related biases in two of those.The software engineering (SE) workforce is dominated by male employees. This is potentially because SE job advertisements are gender biased toward men. SE job advertisements are often checked for potential gender bias using word-based bias detection tools. However, these do not take software engineering specific words into consideration. For example the word 'analyst' is considered male using the word-based tools, and therefore any SE analyst position would be seen (incorrectly) to be biased toward men. As a result, such word-based tools are not good enough to accurately assess for gender bias in SE job ads. We present a more nuanced mechanism to check for gender bias in SE job advertisements by building on the GenderMag method. GenderMag has proven to be successful in gender bias detection in software interfaces. This method identifies five dimensions of problem solving (referred to as facets) on which male and female software users differ. From a survey of software practitioners, we identified factors where male and female SE job applicants differ. We grouped the similar factors together and derived three facets that were important to predict job application behaviour of SE job applicants. We validated these facets with some SE candidates and used them to develop 'new' Tim and Abi personas from the facets and evaluated four SE job advertisements. We identified gender related biases in two of these.