Women Worry, Men Adopt? Gendered Risk Perceptions and Generative AI Adoption

Fabian Stephany and Jedrzej Duszynski

Papers from arXiv.org

Abstract: Generative artificial intelligence (GenAI) is spreading rapidly across work and daily life, yet adoption remains uneven. Men use GenAI more frequently than women, potentially widening inequalities in productivity, skills, and career opportunities. Existing research has largely explained this gap through differences in access, digital skills, and confidence. We argue that these explanations are incomplete: gender differences in GenAI adoption may also reflect how women and men evaluate AI's societal risks. Using two waves (2023-2024) of the nationally representative UK Public Attitudes to Data and AI Tracker (N = 9,172), we combine descriptive analyses with gender-specific, age-stratified random forest models and a parametric score-matching analysis of repeated cross-sections. We first show that men report substantially higher levels of frequent personal GenAI use than women. We then show that this gap is especially pronounced among respondents who express concerns about AI's societal consequences, particularly its effects on mental health and the environment. Intersectional analyses show that the largest disparities arise among younger, digitally fluent individuals with high societal risk concerns, where gender gaps in personal use exceed 45 percentage points. Across predictive models, perceived societal risk has greater predictive relevance for women's adoption than for men's and ranks among the strongest predictors of women's GenAI use. Finally, in score-matched comparisons, higher optimism about AI's societal impact is associated with larger increases in women's uptake, narrowing the gender gap. We interpret these findings as an indication that unresolved AI harms may contribute to unequal access to GenAI's productivity, learning, and career benefits. The findings point to societal risk perception as an important behavioural pathway underlying digital inequality in the AI era.

Date: 2026-01, Revised 2026-07
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