Global Automation Atlas: LLM-Based Study Maps Automation Exposure Across 124 Economies
A new study used a large language model to classify 18,797 work tasks across 124 economies, mapping the share of tasks exposed to automation. The findings show that automation exposure ranges from 3.3% to 61.6% of tasks, with higher-income economies experiencing more labor-augmenting and physical execution automation, while lower-income economies face more rule-based, labor-substituting automation. The research also finds that women are disproportionately employed in occupations with higher substitution-facing exposure. The study highlights how country-level conditions shape automation risk beyond just employment structure.
Why it matters: This research offers a detailed, global perspective on how automation risk varies by country and economic development, with important implications for workforce policy and gender equity.
Full story at: arXiv Computers and Society ↗