Smart AI Regulation Strategies for Latin American Policymakers
The landscape of artificial intelligence (AI) is rapidly evolving, reshaping economies, industries, and public services across the globe. In Latin America, the unique context presents both challenges and opportunities for inclusive and forward-looking AI governance.
The Need for Smart AI Regulation
As generative AI technologies develop, the transformative potential is vast, but so are the associated risks. If left unchecked, AI can deepen inequalities, erode privacy, and widen digital divides. Thus, regulation is not merely a safeguard; it acts as a catalyst for development. Countries with clear regulatory frameworks tend to attract more investment and innovation.
Stages of AI Regulation
AI regulation has progressed through three overlapping stages:
- Ethical Guidelines: Responsible tech principles such as the OECD’s AI principles and UNESCO’s recommendations.
- National Legislation: Formal regulatory frameworks, exemplified by the EU AI Act and NIST’s Management Framework.
- Regional Standards: Coordinated governance initiatives like the Global Partnership on AI and the EU-U.S. Trade and Technology Council.
However, the regulatory journey is not linear. For instance, early 2025 saw the United States pivot towards deregulation, prioritizing innovation over precautionary oversight. This divergence from the EU’s more restrictive stance could potentially deepen global fragmentation.
Latin America’s Regulatory Landscape
Latin American nations are beginning to craft AI regulations inspired by global benchmarks, but progress remains sluggish. Several initiatives have emerged, including regional AI summits and legislative efforts in Brazil and Chile. Yet, AI preparedness in the region still lags behind developed countries and China.
Without the burden of legacy systems, Latin America has the opportunity to leapfrog into governance models that align with both local constraints and global standards.
Designing Smart AI Regulation
To create effective AI regulation, policymakers must strike a balance between safeguarding rights and fostering innovation. A proposed four-part taxonomy reflects both global practices and the unique challenges faced by Latin American countries:
- Enable Innovation: Use regulatory sandboxes to reduce uncertainty.
- Promote Inclusion: Invest in AI literacy and support open-source tools to broaden access.
- Institutionalize Safety: Establish national AI safety institutes to oversee high-risk systems.
- Prevent Monopolization: Reduce regulatory burdens for SMEs to foster competitive neutrality.
Implementation Challenges
Effective regulation requires not only good laws but also capable institutions. Many Latin American countries lack the technical expertise to audit AI systems and enforce compliance. Building this capacity is essential, with recommendations for creating national AI safety units and public-private sandboxes for testing new applications.
Moreover, AI systems must reflect the region’s socioeconomic and cultural realities. For instance, credit algorithms should incorporate alternative data to avoid penalizing the underbanked, and health care AI must prevent diagnostic biases in underserved communities.
Data Sovereignty and Regional Cooperation
Data sovereignty is crucial, with much of the region’s data infrastructure controlled externally. Governments should explore models like the U.K.’s National Data Library to balance privacy with research access. A fragmented regulatory landscape may invite jurisdictional arbitrage, underscoring the need for regional harmonization.
Establishing a Latin American AI governance network could foster regulatory harmonization, share technical capacity, and support joint innovation pilots.
A Flexible and Adaptive Approach
Given the rapid evolution of AI, regulations must be flexible, iterative, and continuously updated to adapt to new developments. This is essential to ensure that regulations evolve alongside the technology.
Conclusion
Latin America stands at a pivotal moment in AI governance. By leveraging its resilience, creativity, and lack of entrenched legacy systems, the region can lead with a smart, inclusive, adaptable, and development-focused approach to AI governance that serves as a model for the Global South and beyond.
