Gender shapes who faces greater workplace AI risks
When AI Enters the Workplace, Who Faces Greater Risks? A Gendered Analysis
Computers and Society
Summary
AI is changing many jobs, but not everyone is affected the same way. The authors found that jobs mostly held by women face different kinds of AI changes than those mostly held by men. Women's jobs, especially lower-paid ones, might see more automation and fewer chances for raises or career growth. This means AI could increase inequalities between men and women at work.
What this means in practice
- •For hr managers: Identify which roles may need targeted retraining programs based on gender-related AI exposure risks to reduce workforce inequality.
- •For workforce planners: Adjust workforce strategies to anticipate differential impacts of AI on male- and female-dominated jobs, focusing on vulnerable lower-skilled roles.
Authors
Miriam Fernandez, Ángel Pavón Pérez, Damiano Giallongo, Davide Ghia, Maryam Yaqub, Daniele Quercia, Tania Cerquitelli
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 associated with task automation, job restructuring, reduction of wages and limited career progression.