Unlocking Global Development with Decentralized AI

Unlocking Global Development with Decentralized AI | Opinion

Artificial Intelligence (AI) has become a significant part of our lives, impacting everything from work productivity to personal issues. However, its current centralized structure limits its benefits, particularly for the underserved populations around the globe. The United Nations aims to achieve 17 Sustainable Development Goals (SDGs) by 2030, and leveraging decentralized AI could help make equitable growth, poverty eradication, and climate action a reality. The centralized AI model is rife with data privacy issues, high costs, and reduced accessibility, hindering its effectiveness in the Global South. To fulfill its potential for good, a shift towards decentralized AI is essential.

Key Takeaways

Current AI systems are largely centralized, dominated by major tech companies, resulting in power imbalances and data privacy concerns.
Centralized AI fails to meet the unique needs of developing regions, leading to harmful outcomes in addressing the UN’s 17 Sustainable Development Goals (SDGs).
Decentralized AI, supported by technologies like federated learning and blockchain, is providing promising solutions that enhance community empowerment and accountability.
The global development community is urged to focus on funding the development of open, decentralized AI infrastructures to foster sustainable growth.

The challenge now is for the global development community to prioritize funding the adoption of open, decentralized AI infrastructures over corporate tools

The Centralization Challenge

AI is already being utilized to tackle issues ranging from healthcare to climate change. However, its evolution is predominantly under the control of a few major companies, whose systems are not equipped for the unique challenges related to the UN’s 17 SDGs. The problem lies not in the technology itself but in the governance surrounding it. Centralized AI models can create significant barriers that hinder positive impacts on development.

These models typically rely on data from a small number of developed areas, neglecting regions in the Global South. This results in AI applications—like disease diagnoses and financial risk predictions—being ineffective when applied in diverse, underrepresented contexts. Such oversight can cause serious misidentification, denial of services, and exacerbate socioeconomic gaps, particularly affecting SDG 10, which focuses on improving social and economic inclusion.

Additionally, centralized systems require sensitive local data to be stored on distant servers, creating security vulnerabilities and violating local data sovereignty—this directly threatens SDG 16, which advocates for peace and strong institutions. Such practices have led nations like Singapore and Malaysia to develop their own sovereign AI technologies in order to protect their data.

Perhaps the critical issue is accountability. When a centralized AI system makes an error that impacts millions, who is responsible? The “black box” nature of these systems complicates auditing decisions about crucial matters like aid distribution, making it troublingly difficult to ensure ethical governance regarding high-stakes development work.

To truly harness AI for global betterment, there must be a fundamental transition from corporate-controlled AI models to frameworks that prioritize inclusion, data sovereignty, and accountability.

Decentralized AI: A Dual Solution

Decentralized AI, empowered by federated learning and blockchain technology, is gaining traction as a potential remedy for these issues. The SDG Blockchain Accelerator Programme, led by the UNDP and collaborating with partners such as Blockchain for Good Alliance and Stellar, showcases initiatives aimed at empowering communities in the Global South.

Federated learning trains AI models across multiple decentralized devices while safeguarding local data. For instance, projects in Latin America and the Caribbean utilize this technology to collaboratively develop predictive AI models that assess climate risks without compromising sensitive data. This approach enables fair assistance to vulnerable farmers and female-owned businesses, aligning with SDG 13 (climate action) and SDG 5 (gender equality).

Incorporating blockchain technology further enhances these projects by replacing single corporate entities with an immutable and transparent governance system, fostering accountability and collaborative infrastructure. In Liberia, smart contracts and decentralized AI streamline aid distribution transparently. Meanwhile, in Kenya, decentralized AI is improving payment accuracy for local businesses, supporting economic growth and fostering trust in public institutions—key components of SDG 8 (decent work & economic growth) and SDG 10 (reduced inequalities).

Moreover, decentralized technologies are being explored in various sectors, like conservation efforts pairing Blockchain-based NFTs developed by Cambridge University and UNDP Rwanda for mountain gorilla protection. They also enhance hospital record security in Africa, granting patients control over their medical data, thereby contributing to SDG 15 (life on land) and SDG 3 (good health & well-being).

Advocating for Responsible Frameworks

While AI holds tremendous potential, its challenges stem primarily from its governance approach. The current centralized model contradicts the UNDP's core principles of inclusion, sovereignty, and accountability. However, emerging initiatives reveal a viable ethical and scalable alternative.

The global development community must now focus on funding the transition to open, decentralized AI infrastructures rather than relying on corporate tools that hinder progress. By shifting from passive consumption to active stewardship of intelligence, we can strive for a sustainable future, particularly for those in the world’s most disadvantaged areas.