Global Trends in AI Governance Evolving Country Approaches

What’s Covered?

As AI becomes embedded in public services, industry, and economic infrastructure, governments are responding with diverse and evolving governance strategies. This report synthesizes policy mechanisms and offers a framework for understanding which governance tools are used where, why, and how they are evolving.

Foundational Ecosystem Needs (Section 2):

Before regulation, countries must build enabling conditions. These include:

Digital and data infrastructure (reliable internet, cloud computing, open datasets)
Human capital (digital literacy, local AI talent pipelines)
Local ecosystems (entrepreneurship, access to funding, innovation clusters)

Regulatory Tools (Section 4):

Four mechanisms shape the current regulatory toolbox:

Industry Self-Governance
- Can shape AI practices from within companies
- Often lacks enforceability or credibility in high-risk sectors
Soft Law
- Includes national principles, international charters, and voluntary codes
- Flexible but hard to enforce or measure
Hard Law
- Creates clear rights and duties (e.g. GDPR, EU AI Act)
- Slow to adapt and resource-intensive
Regulatory Sandboxes
- Allow safe testing of AI in live environments
- Enable learning-by-doing but limited in scope and scalability

Governance Challenges (Section 3):

The report flags critical challenges:

Bias and Fairness: Existing inequities can be encoded and magnified by AI unless actively mitigated
Data Privacy and Security: Reliance on large datasets demands stronger safeguards and privacy engineering
Transparency and Accountability: Trust requires systems to be explainable and developers to be accountable
Sector-Specific Risks: AI in health, finance, or government services can cause harm at scale if not regulated carefully

Stakeholder Roles (Section 6):

Effective governance requires collaboration across:

Public regulators (e.g. data protection authorities, digital ministries)
Private sector (AI developers, platform operators)
Civil society (advocacy groups, communities impacted by AI)
International bodies (e.g. OECD, UNESCO, GPAI)

Guidance for Policymakers (Section 7):

Key recommendations include:

Engage all stakeholders to ensure inclusive and accountable policy
Tailor laws and tools to local contexts rather than copy-pasting foreign models
Balance regulatory ambition with infrastructure readiness
Align with global norms to avoid fragmentation and regulatory arbitrage
Integrate legacy regulation (data protection, labor, consumer safety) into new AI laws

💡 Why it matters?

This report makes the case that AI governance cannot be imported wholesale—it must be built locally, shaped by local infrastructure, institutions, and values. Its global scan is especially valuable for lower- and middle-income countries weighing where to begin. The World Bank’s framing gives national governments a map, not just a mirror.

What’s Missing?

The report sidesteps enforcement capacity. While it offers regulatory options, it doesn’t tackle the on-the-ground challenges of implementation—like funding regulators, upskilling judges, or setting up effective audit regimes. There’s also limited attention to frontier risks (e.g. model misuse, model collapse, dual-use concerns) and how developing countries might deal with the AI capabilities of foreign platforms operating within their borders.

Another gap: while international coordination is recommended, there’s little practical guidance on how to harmonize governance across uneven institutions, especially where states have conflicting commercial interests in AI.