联合国贸易发展委员会-2025年技术与创新报告（英）_182页_11mb

Technology and Innovation Report 2025: AI and Development Challenges

Overview

Artificial Intelligence (AI) is rapidly reshaping the global economy, offering immense potential for productivity gains but also posing significant risks, particularly the exclusion of developing countries. The United Nations report highlights the dominance of tech giants from developed nations, unequal access to AI infrastructure, and the urgent need for equitable AI governance to ensure shared prosperity.

Key Challenges

1. Unbalanced AI Adoption

• Digital Infrastructure Gaps:
  Developing countries face significant infra-stech hurdles due to insufficient electricity access, unreliable Internet connectivity, and limitations of last-mile digital networks. For example, over 3 billion people worldwide lack Internet access, mostly in rural areas of Africa and Asia (ITU, 2022). This limits AI experiments and AI development priorities (Table III: Challenges in AI Preparedness).

• Skills and Data Deficiencies:
  AI models require specialized skills in data science and machine learning, often concentrated in developed economies. Basic digital literacy, while improving, remains low in many regions (Figure III.11). Access to high-quality, diverse data is also hampered by limited digital resources and fragmented data governance.

• Funding Constraints:
  Public R&D allocations remain too low in many developing countries, limiting AI innovation capabilities. For instance, China invests heavily in AI-related academic papers, while most developing countries lag significantly (Table III.1).

2. Economic and Ethical Concerns

• Labor Market Disruption:
  AI threatens to displace jobs in knowledge-intensive sectors more than physical ones, exacerbating inequality (Acemoglu et aI, 2022). While emerging markets may benefit from efficiency gains, workers in low-income countries often lack resilience (Brynjolfsson et al., 2023).

• Data Sovereignty and Governance:
  AI systems rely on global data flows, prioritizing private sector control over public oversight. Current regulations may not fully address risks of bias, misinformation, or unethical use (OECD, 2019; Kretschmer et al., 2023).

• Concentration Risks:
  The dominance of U.S. and other tech giants at the technological frontier restricts developing country access to AI ecosystems, limiting localized innovation (UNCTAD, 2022).

Proposed Solutions

1. Digital Public Infrastructure (DPI)

• Shared, interoperable digital platforms can address infrastructure gaps by offering affordable access to computing power and data analytics (UNDP, 2023). Governments should prioritize AI as a public good, ensuring equitable access through mechanisms like the "CERN model" for resources.

2. Global AI Governance

• Harmonized international standards can ensure accountability by requiring transparency disclosures, impact assessments, and mandatory certification for high-risk AI systems (ILO, 2024). The UN Pact for the Future provides a framework for multi-stakeholder collaboration.

3. Capacity Building

Developing strong AI ecosystems requires training human capital through STEM education and targeted upskilling programs. Frontier regions like Africa must leverage open-source tools and regional cooperation to build resilient AI capabilities (UNICEF, 2023).

4. South-South Collaboration

• African and Asian nations can form alliances to co-develop AI solutions tailored to regional needs, fostering sustainable innovation (UNCTAD, 2024a; Beraja et al., 2024).

Conclusion

The dominance of AI development by privileged actors risks perpetuating global inequalities. Moving forward requires a multi-pronged approach: equitable access to infrastructure, ethical governance frameworks, capacity-building for developing nations, and robust international cooperation. By placing human-centric values ahead of profit motives, societies can harness AI’s transformative potential for inclusive development aligned with the Sustainable Development Goals.