Buffer or Bottleneck? Employment Exposure to Generative AI and the Digital Divide in Latin America
This joint ILO and World Bank working paper estimates the exposure of Latin American and Caribbean labor markets to generative AI by leveraging harmonized household and labor force surveys. It adjusts occupational exposure measures to account for the slower pace of technology adoption and the digital divide, evaluating whether digital infrastructure barriers prevent workers from realizing potential productivity gains. The findings indicate that urban jobs requiring higher education and formal status face higher exposure, while lack of digital access significantly limits potential augmentation benefits across the region.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, policymakers, and international organizations seeking empirical analysis on how generative AI impacts labor markets and the digital divide in developing economies.
What the document contains
The document addresses the estimated exposure of the Latin American and Caribbean labor market to generative AI, categorized by automation, augmentation, and the big unknown. It evaluates socio-economic characteristics of exposed workers and adjusts these measures using the likelihood of accessing digital technologies and computers at work.
The document does not provide binding regulatory frameworks, legal compliance mandates, or specific enforcement mechanisms for governing artificial intelligence.
- Between 30 and 40 percent of employment in Latin America and the Caribbean is exposed in some way to generative AI.
- Only a small portion of occupations, between 2 to 5 percent, falls into the potential category for full automation.
- Jobs with high exposure tend to be urban-based, require higher education, belong to the formal sector, and are held by individuals with higher relative incomes.
- Inadequate digital infrastructure and a lack of computer use at work act as major bottlenecks preventing nearly half of potential augmentation jobs from realizing productivity benefits.
- Women and young workers face higher exposure to potential automation risks, particularly in finance, insurance, and public administration.
· Abstract · About the authors · Acronyms · Introduction · LAC region and the theoretical effects of GenAI · Methods · Findings · Final discussion · Appendix · References · Acknowledgements
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
n/a
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
Historically, together with Sub-Saharan Africa, LAC is one of the most unequal regions in the world (World Bank, 2016a), with levels of income inequality strongly influenced by the changes in the structure of the labour market
1 LAC region and the theoretical effects of GenAI
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
n/a
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
n/a
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
n/a
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
n/a
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
n/a
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
n/a
screening score · 2026-10-23