women at higher risk

While artificial intelligence promises to transform workplaces around the world, women face a disproportionate threat of losing their jobs to automation. A recent UN International Labour Organization report shows women’s jobs in high-income countries are three times more likely to be automated by AI compared to men’s roles.

The numbers paint a stark picture. Nearly 10% of female-dominated positions in wealthy nations face automation risk, versus just 3.5% for men. Overall, 41% of women’s jobs in high-income countries could be exposed to AI, compared to 28% for men.

Women’s jobs in wealthy nations face nearly triple the automation risk compared to men’s positions.

This gap exists because women are concentrated in administrative and clerical support roles. These jobs involve repetitive tasks that AI can easily handle. In the US, 79% of employed women work in high-risk automation jobs, compared to 58% of men. Data entry clerks and accountants face particularly high exposure to AI automation, according to the analysis. Female-dominated occupations like cashiers and secretaries are increasingly being replaced by AI systems.

The problem varies by region. In Europe and Central Asia, 39% of women’s jobs are susceptible to AI exposure compared to 26% for men. Researchers analyzed over 1,640 workers and 2,500 professions to map these risks across different areas.

The gender gap in tech fields makes things worse. Women are underrepresented in AI and tech roles, which could create new disadvantages by 2025. Without opportunities or training in AI-related fields, women can’t easily shift to safer jobs. This situation mirrors broader workforce trends where job market evolution is favoring skilled labor while diminishing routine work.

There’s also concern about algorithmic bias. AI systems trained on biased data can discriminate against women in hiring, promotions, and evaluations. Without proper safeguards, these systems might deepen existing workplace inequalities.

AI could change what skills matter for leadership, potentially emphasizing adaptability and emotional intelligence. But traditional biases against women leaders might persist in new forms.

The research combined national employment data with expert analysis to understand these patterns. One in four workers globally faces some AI exposure, but women in higher-income countries bear the greatest risk.

These findings suggest that as AI reshapes the workforce, women need targeted support and training. The gap in STEM education and digital literacy must be addressed. Otherwise, the promise of AI could widen gender inequality rather than reduce it.

References

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