When AI Enters the Workplace, Who Faces Greater Risks? A Gendered Analysis

arXiv:2609.21756v1 Announce Type: new Abstract: 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 amplifies existing labor market disparities, harming women's economic security.

The analysis shows that AI exposure, especially via LLMs, disproportionately affects female-dominated occupations, including low-skilled and low-paid roles. This suggests heightened risk of job automation, wage suppression, and limited career progression for vulnerable women, potentially exacerbating existing gender inequalities in the labor market.

The study presents correlational evidence that AI exposure patterns differ by gender composition of occupations, with lower-paid female-dominated jobs potentially more exposed to automation-related displacement. This is a structural concern grounded in occupational data, but conclusions about actual harm rely on triangulation with other literature rather than direct outcome measurement.

The paper finds that AI exposure, particularly from LLMs, is high in female-dominated occupations across all skill and wage levels. This disproportionate exposure, especially for vulnerable women in lower-skilled and lower-paid roles, suggests increased risks of task automation, wage reduction, and limited career progression due to AI.

The paper presents evidence that AI-driven innovation may amplify existing gender inequalities in the labor market. Women in vulnerable positions may face greater risks of job automation and wage reduction.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   30a93b58-66e8-4199-b166-2070b0df19b6
           https://arxiv.org/abs/2609.21756
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EVIDENCE   3 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -61  (Adverse)
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CONFIDENCE 80%