Artificial intelligence’s labor market transformation may affect workers differently based on gender, though this dimension receives insufficient analytical attention. Occupational segregation by gender means AI’s task-based automation could have disparate impacts on women and men workers. Understanding and addressing these gender dimensions should inform policy responses.
Research shows 60% of jobs in advanced economies will be affected by AI, with 40% of positions globally facing similar changes. However, aggregate figures may mask gendered patterns in which specific jobs and tasks face automation. The approximately 10% of jobs already enhanced by AI may also show gender disparities in access to augmentation.
Young workers entering the labor force may face gendered barriers as entry-level positions disappear. If automation disproportionately affects occupations where women or men predominate, this could exacerbate existing gender inequalities in employment and earnings. This intersectional analysis remains underdeveloped in AI impact assessments.
Middle-class workers in female-dominated or male-dominated occupations may experience AI’s effects differently. Gendered patterns in which jobs receive productivity-enhancing AI investment versus elimination could affect wage gaps and career opportunities. These gender dimensions deserve explicit analysis.
Governance frameworks should explicitly address potential gender disparities in AI’s labor impacts. Policies designed without attention to gender may inadvertently worsen inequalities. Labor organizations increasingly emphasize intersectional approaches that consider how AI affects workers differently based on multiple dimensions including gender. International cooperation on gender-sensitive AI governance faces challenges but could identify effective approaches.