The Global Majority in International AI Governance

Technical Summary: The Global Majority in International AI Governance

Problem Statement
The paper identifies a systemic "Global AI Divide" characterized by profound disparities in AI development, innovation, and regulation between Western nations (the Global North) and countries in Africa, Asia, the Caribbean, Latin America, and Oceania (the Global Majority). The authors argue that the current concentration of computational resources, diverse datasets, and technical expertise in the West—particularly the US, UK, Canada, and China—has created a cycle of technological dependency. This dynamic forces Global Majority nations to adopt AI systems optimized for Western socio-cultural contexts, often resulting in suboptimal outcomes, the erosion of local innovation ecosystems, and a form of "technological colonialism." Furthermore, global AI governance frameworks are dominated by Western corporations and states (e.g., the EU's AI Act and OECD principles), which frequently sideline the unique priorities, values, and infrastructural realities of the Global Majority, thereby exacerbating existing global inequalities.

Methodology
This work is a qualitative policy analysis and literature review rather than an empirical experimental study. The authors synthesize existing data, reports, and policy documents to map the landscape of global AI governance. The methodology involves:

1. Systematic Review of Disparities: Analyzing data on educational access, STEM literacy, research output, talent migration (brain drain), and infrastructure costs (compute and connectivity) to quantify the "AI divide."
2. Comparative Policy Analysis: Examining and contrasting emerging AI governance initiatives in the Global Majority (specifically the African Union Continental AI Strategy, ASEAN Guide, and Latin American declarations) against dominant Western frameworks like the EU AI Act and OECD Principles.
3. Mapping Governance Participation: Reviewing the involvement of Global Majority nations in international conventions, standards bodies (e.g., Council of Europe, UNESCO, Public Voice), and high-level summits to assess the degree of their agency in global decision-making.
4. Case Study Identification: Highlighting specific regional efforts, such as the Global AI Summit on Africa and the Commonwealth Artificial Intelligence Consortium, to illustrate emerging counter-trends.

Key Contributions
The paper makes several distinct contributions to the discourse on AI governance:

• Conceptualization of the "Global Majority": It explicitly frames the analysis through the lens of the "Global Majority" (countries where communities have been racialized into ethnic minority groupings but constitute the global demographic majority), shifting the focus from a "developing vs. developed" binary to a structural power analysis.
• Documentation of Structural Inequities: It details how inequalities in education (learning poverty, lack of English-language AI curricula), research (underrepresentation in top conferences), and infrastructure (high costs of training models, low internet penetration) create a self-reinforcing cycle of exclusion.
• Cataloging Indigenous Governance Efforts: The paper provides a comprehensive inventory of AI strategies and legislative drafts emerging from the Global Majority, including the AU Continental AI Strategy (which prioritizes African values like Ubuntu and addresses specific environmental risks), the ASEAN Guide on AI Governance, and various national bills in Nigeria, Kenya, Brazil, and Chile.
• Critical Analysis of the "Brussels Effect": It critiques the global diffusion of EU regulatory standards, arguing that while influential, they often fail to account for the distinct economic landscapes and enforcement capacities of Global Majority nations.

Results and Findings

• Governance Centralization: Global AI governance remains highly centralized, with Western nations and corporations setting de facto standards. While the EU AI Act and OECD principles are widely cited, Global Majority countries are often relegated to observer roles or invited to sign pre-drafted agreements rather than co-creating them.
• Emerging Regional Autonomy: Despite centralization, significant countertrends are emerging. The African Union has released a non-binding but comprehensive strategy that categorizes risks differently (focusing on environmental and structural risks specific to Africa) compared to the EU. Similarly, ASEAN and Latin American bodies are developing risk-based frameworks that align with regional values and the UNESCO Recommendation on the Ethics of AI.
• Capacity Gaps: The analysis confirms that Global Majority countries face severe barriers in AI R&D, including a lack of compute infrastructure (with the US holding nearly half of global data centers) and a "brain drain" where top-tier AI talent migrates to Western hubs.
• Inclusion Initiatives: Recent events, such as the Global AI Summit on Africa (hosted by Rwanda) and the formation of the Commonwealth Artificial Intelligence Consortium, signal a shift toward South-South cooperation and a demand for leadership roles in global AI discourse.

Significance and Claims
The paper claims that achieving equitable global AI governance requires moving beyond superficial inclusion to systemic reform. It argues that without addressing the root causes of the AI divide—specifically the redistribution of resources, capacity building in education and research, and the restructuring of international decision-making processes—AI governance will continue to deepen global disparities rather than alleviate them.

The authors conclude that the significance of this work lies in its actionable roadmap for democratizing AI governance. They propose specific mechanisms to amplify the agency of Global Majority countries, including:

• Establishing regional AI governance hubs in emerging talent centers (e.g., Nairobi, São Paulo, Jakarta).
• Creating dedicated funding mechanisms (e.g., AI development bonds) to support local research and policy development.
• Restructuring international forums to include rotating leadership and decision-making power for Global Majority regions.
• Developing inclusive technical standards that account for resource-constrained contexts (e.g., low-bandwidth processing).

Ultimately, the paper asserts that for AI to serve as a catalyst for shared prosperity, governance frameworks must be co-created to reflect the diverse cultural, economic, and ethical priorities of the Global Majority, ensuring that AI development does not reinforce historical patterns of exclusion.