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

Women in lower-skilled jobs face disproportionate AI exposure: Unlike male-dominated occupations where AI exposure concentrates in higher-skilled, higher-paid roles, female-dominated occupations show "relatively uniform levels of exposure across both high-skilled, high-paid, and low-paid occupations," meaning women in vulnerable positions lack the protective buffer of skill level.

Large Language Models pose particular risks to female-dominated work: The paper finds that "LLM-related exposure is higher in female-dominated occupations," suggesting that recent advances in AI technology may be more likely to automate tasks traditionally performed by women, potentially accelerating job displacement in these sectors.

Existing gender wage gaps may be amplified by AI: The research indicates that "existing disparities may be unintentionally amplified through task automation, unequal access to upskilling opportunities, and differential returns obtained from technological change," meaning AI adoption could worsen the gender inequality that already shapes women's lifetime earnings.

Women have unequal access to AI-driven opportunity: While broader AI innovation remains concentrated in male-dominated occupations (which tend to offer better wages and advancement), women are more exposed to the automatable aspects of AI, creating a scenario where men may benefit more from new AI-related job creation and upskilling opportunities.

Vulnerable women workers need targeted protection: The paper's focus on "women, particularly those in the most vulnerable positions (lower-skilled and lower-paid)" suggests that policy interventions should prioritize support for this group to prevent AI from deepening existing economic inequalities.