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Artificial Intelligence and Heterogeneous Unemployment Risk Across Regions: Scenario-Based Projections of Alternative
Toby Kai-Bo Shen1, Melody Hsiao-San Yeh1, Hsiao-Hui Chen2
1From the Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan (T.K.-B.S., M.H.-S.Y., Q.W., and Y.-C.C.).
Objective:
The aim of this study was to evaluate the impact of artificial intelligence (AI) on employment in Taiwan by quantifying exposure and projecting unemployment risks across industries, occupations, and regions.
Methods:
National workplace survey data (N = 4009) were analyzed using AI Industry Exposure and Occupation Exposure indices to construct a composite artificial intelligence exposure combined indicator. Six scenarios (α = 0.075 or 0.15; retraining adjustment = 0, 0.5, 1) modeled unemployment projections for 2025-2035.
Results:
Taipei, Hsinchu, and Taichung showed the highest exposure. Under high-impact scenarios, urban unemployment may rise sharply, whereas retraining interventions reduced projected risks. Rural regions remained less affected.
Conclusions:
AI exposure is unevenly distributed, concentrating risk in technology-intensive regions and occupations. Targeted workforce adaptation policies are needed to mitigate unemployment and regional disparities.
