AI Vision for a Fair Global Order

Executive Summary: Artificial Intelligence (AI) is emerging as the defining technology of the 21st century. The United Nations has recognized AI’s potential to transform economies, societies, and governance, while also raising risks of inequality, misuse, and instability. Developing nations must seize this momentum and craft National AI Action Plans to ensure AI serves their economic growth, public sector transformation, and digital sovereignty.

1. The UN’s Emerging AI Vision for Global Order

The UN’s approach to AI emphasizes inclusivity, safety, and human-centered development:

• UNGA Resolution (2024): Called for safe, secure, trustworthy AI aligned with human rights.
• Global Digital Compact: Outlined cooperation on AI standards, governance, and capacity-building.
• HLAB-AI Final Report: Recommended global AI fund, standards exchange, and capacity-building networks.
• UNESCO Ethics of AI (2021): Established global baseline on transparency, accountability, and fairness.

2. Why Developing Nations Need an AI Action Plan

Developing nations face a dual challenge: harnessing AI for sustainable development while avoiding deepening global inequalities. Key reasons for adopting National AI Action Plans:

1. Economic opportunity in agriculture, healthcare, education, and logistics.
2. Public sector transformation through efficient service delivery.
3. Governance and safety against misinformation, cybercrime, and bias.
4. Strategic autonomy in data, compute, and talent to avoid dependency on major AI powers.

3. Pillars of a National AI Action Plan

A robust national AI strategy should rest on the following pillars:

• A. Governance & Rights: National AI Council, impact assessments, accountability.
• B. Data & Compute: Public-interest data commons, sovereign compute hubs.
• C. Talent & Institutions: AI curricula, scholarships, diaspora engagement, public AI labs.
• D. Sectoral Transformation: Agriculture (yield prediction), Health (AI triage), Education (AI tutors), Public Finance (procurement monitoring).
• E. Innovation & Inclusion: Regulatory sandboxes, SME AI adoption, local-language models.
• F. Safety & Resilience: AI-CERT for misuse detection, deepfake monitoring, election integrity safeguards.

4. Financing & Partnership Architecture

To realize AI’s potential, financing and partnerships must be carefully structured:

• Blended Finance: Public, private, and development capital pooled for AI infrastructure.
• South–South Cooperation: Regional compute hubs and shared AI models.
• Development Partners: UNCTAD, UNDP, and multilateral banks to support national AI strategies.

5. Governance Model: Institutions and Processes

Governance structures must be inclusive and transparent:

• National AI Council: with academia, industry, and civil society.
• Chief AI & Data Officer: for whole-of-government coordination.
• Standards & Sandboxes Unit: to adapt global standards locally.
• Public consultations and transparency reports: aligned with UN frameworks.

6. Policy Guardrails (Mapped to UN/UNESCO)

Policy principles include:

• Human rights by design.
• Accountability and auditability.
• Safety and security for high-risk systems.
• Fairness and non-discrimination.
• Data privacy and governance.
• Environmental responsibility in compute and data centers.

7. 12–24-Month Action Roadmap

• 0–3 Months: Establish National AI Council, Chief AI Officer, launch pilot sandboxes.
• 4–12 Months: Set up Public AI Lab, roll out AI-CERT, regional data/compute partnerships.
• 12–24 Months: Scale pilots nationally, publish transparency reports, launch blended finance facilities.

8. Measurement & KPIs

Metrics should track inclusion, capacity, impact, and governance:

• Inclusion: Women/youth in AI programs.
• Capacity: Number of trained civil servants, AI infrastructure uptime.
• Impact: Reduction in crop loss, health triage times, procurement savings.
• Governance: Audited systems, resolved public complaints.

9. Risk Register & Mitigations

Key risks include:

• Misinformation and deepfakes – mitigated by provenance tools.
• Bias and exclusion – mitigated through audits and inclusive datasets.
• Vendor lock-in – addressed via open standards and dual-vendor strategies.
• Skills gap – mitigated through scholarships, fellowships, and extension services.

10. Conclusion

The UN has set out the global direction for AI governance. Developing nations must now translate these principles into actionable strategies. By investing in AI capacity, governance, and innovation, they can leapfrog in development, protect sovereignty, and help shape a fairer global AI order.