arXiv:2601.03880econ.GNcs.AI2026-01被引 1

女性更担忧AI风险,导致使用率低于男性,加剧数字不平等。

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

  • 通过分析英国万人数据,发现性别差异源于对AI社会风险的不同评估。
  • 风险担忧越强的群体中,男女使用GenAI差距超45个百分点。
  • 提升对AI的乐观预期可显著缩小女性使用差距,适合政策制定者参考。

生成式人工智能(GenAI)正快速渗透工作与日常生活,但使用率存在明显性别差异:男性使用频率显著高于女性,可能加剧生产力、技能与职业机会的不平等。现有研究多从获取渠道、数字技能和自信程度解释此差距,但我们认为这并不完整——性别差异还反映在对AI社会风险的评估上。基于2023–2024年英国全国代表性公众数据与AI态度追踪调查(N=9,172),我们结合描述性分析、分性别年龄的随机森林模型及重复横截面的参数化得分匹配分析,发现:男性报告频繁使用GenAI的比例远高于女性;这一差距在关注AI社会后果(尤其心理健康与环境影响)的群体中尤为显著。交叉分析显示,年轻、数字熟练且高度担忧社会风险者中,男女使用差距超过45个百分点。预测模型表明,感知社会风险对女性使用行为的预测力强于男性,是关键预测因子之一。得分匹配结果显示,对AI社会影响更乐观者,女性采用率提升更明显,性别差距缩小。研究提示,未解决的AI潜在危害可能是造成数字不平等的重要行为路径。

原文摘要 · Abstract (English)

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.

AI风险性别差异生成式AI数字不平等

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