AI 中文总结
本研究基于新数据集分析AI暴露在男性和女性主导职业中的差异，发现女性主导职业暴露更均匀且LLM相关暴露更高，低技能低薪女性面临更大风险。
AI 中文摘要
性别不平等仍然是劳动力市场的一个持久结构性特征，塑造了女性的终身收入和经济安全。随着人工智能（AI）改变组织实践，人们日益担忧现有差距可能通过任务自动化、技能提升机会的不平等获取以及技术变革带来的差异化回报而被无意放大。在本文中，我们考察了AI驱动创新在不同男性主导和女性主导职业中的暴露程度如何变化，特别关注技能和工资分布上的差异。利用一个将职业特征与AI暴露度量联系起来的新数据集，我们分析了大型语言模型（LLMs）和更广泛AI技术的最新进展如何在劳动力市场中分布。我们的研究结果表明，虽然AI暴露通常集中在男性主导职业中技能更高、薪资更高的岗位，但女性主导职业在高技能高薪和低技能低薪职业中显示出相对均匀的暴露水平。此外，我们发现与LLM相关的暴露在女性主导职业中更高，而与更广泛AI创新相关的暴露则更集中在男性主导职业中。将这些结果与现有文献进行三角验证表明，女性，尤其是那些处于最脆弱地位（低技能和低薪的女性主导职业）中的女性，可能面临与任务自动化、工作重组、工资降低和职业发展受限相关的AI形式的更大暴露。
英文摘要
Gender inequality remains a persistent structural feature of the labour market, shaping women's lifetime earnings and economic security. As artificial intelligence (AI) transforms organisational practices, there is growing concern that existing disparities may be unintentionally amplified through task automation, unequal access to upskilling opportunities, and differential returns obtained from technological change. In this paper, we examine how exposure to AI-driven innovation varies across male- and female-dominated occupations, with particular attention to differences across the skill and wage distribution. Using a novel dataset that links occupational characteristics to measures of AI exposure, we analyse how recent advances in Large Language Models (LLMs) and broader AI technologies are distributed across the labour market. Our findings show that, while AI exposure is generally concentrated in higher-skilled and higher-paid occupations for male-dominated occupations, female-dominated occupations display relatively uniform levels of exposure across both high-skilled, high-paid, and low-skilled, low-paid occupations. Moreover, we find that LLM-related exposure is higher in female-dominated occupations, while exposure to broader AI innovation remains more concentrated in male-dominated occupations. A triangulation of these results with existing literature suggests that women, particularly those in the most vulnerable positions (lower-skilled and lower-paid female-dominated occupations), may face greater exposure to forms of AI associated with task automation, job restructuring, reduction of wages and limited career progression.
Comments12 pages, 5 figures, 8 tables. Accepted at the 2026 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026)
