Skills-based matching promises mobility of workers between different sectors and occupations in the labor market. In this case, job seekers can look for jobs they do not yet have experience in, but for which they do have relevant skills. Currently, there are multiple occupations with a skewed gender distribution. For skills-based matching, it is unclear if and how a shift in the gender distribution, which we call gender mobility, between occupations will be effected. It is expected that the skills-based matching approach will likely be data-driven, including computational language models and supervised learning methods. This work, first, shows the presence of gender segregation in language model-based skills representation of occupations. Second, we assess the use of these representations in a potential application based on simulated data, and show that the gender segregation is propagated by various data-driven skills-based matching models.These models are based on different language representations (bag of words, word2vec, and BERT), and distance metrics (static and machine learning-based). Accordingly, we show how skills-based matching approaches can be evaluated and compared on matching performance as well as on the risk of gender segregation. Making the gender segregation bias of models more explicit can help in generating healthy trust in the use of these models in practice.

翻译：基于技能的匹配有望促进劳动力市场中不同行业和职业间的工人流动性。在此情况下，求职者可寻找自身尚无经验但具备相关技能的岗位。当前，许多职业存在显著的性别分布失衡。对于基于技能的匹配而言，尚不明确职业间性别分布的变化（即本文所称的性别流动性）将如何或是否会受到影响。预计基于技能的匹配方法将依赖于数据驱动，包括计算语言模型和监督学习方法。本研究首先揭示了基于语言模型的职业技能表征中存在性别隔离现象。其次，我们基于模拟数据评估了这些表征在潜在应用中的使用情况，结果表明各类数据驱动的基于技能的匹配模型会加剧性别隔离。这些模型基于不同的语言表征（词袋模型、word2vec和BERT）及距离度量（静态与基于机器学习的方法）。据此，我们展示了如何从匹配性能以及性别隔离风险两方面对基于技能的匹配方法进行评估与比较。使模型的性别隔离偏差更加明确，有助于在实践中对这些模型的使用建立健康的信任。