Decentralized AI is poised to become one of the most powerful levers for global development. The real task now is not to invent ever more sophisticated models, but to reimagine the infrastructure beneath them so that open, decentralized systems are prioritized over closed, corporate-controlled ones.
Artificial intelligence already shapes daily life, from task automation and logistics optimization to mental health support and creative work. Yet the places where AI is most visible are rarely the places where it is most needed. For people living in fragile states, rural communities, and low-income regions, the dominant form of AI innovation has made only a marginal difference. The problem is not a lack of potential, but a flawed architecture.
The United Nations Development Programme has framed its efforts around 17 Sustainable Development Goals, ranging from eradicating extreme poverty and hunger to expanding access to healthcare, quality education, and climate resilience by 2030. On paper, AI appears to be a natural ally in this agenda: it can analyze climate data to predict floods, optimize crop yields, detect disease outbreaks, and root out corruption. But the way AI is currently built and governed makes it ill-suited to deliver on these promises where they matter most.
The contemporary AI ecosystem is highly centralized. A small number of technology corporations, largely concentrated in a few wealthy countries, control the most advanced models, the underlying infrastructure, and the vast troves of data required to train them. This concentration of power is not a neutral technical choice; it has deep social and political consequences. It raises costs, restricts access, magnifies privacy risks, and entrenches global inequalities rather than reducing them.
This “centralization paradox” is stark: a technology that could, in theory, democratize intelligence and opportunity is instead reinforcing existing hierarchies. Models are overwhelmingly trained on data from a limited set of high-income regions, using languages, cultural assumptions, and institutional norms that do not reflect the realities of many societies in the Global South. When those models are exported into unfamiliar contexts, the results can be not simply inaccurate, but harmful.
Studies have shown that centralized models deployed for medical diagnosis, credit scoring, or risk assessment in regions they were never trained for frequently misclassify individuals, with cascading effects. Misdiagnosis can lead to delayed or inappropriate treatment. Biased credit risk models can deny loans or insurance to entire communities. Predictive policing tools trained on skewed crime data can deepen discrimination. These failures do not just represent technical glitches; they actively undermine SDG 10, which calls for reducing inequality and promoting social, economic, and political inclusion for all.
Centralized AI systems also depend on aggregating massive volumes of sensitive local data—health records, financial histories, criminal justice information—onto remote corporate servers. Such concentration makes the data a lucrative target for cyberattacks. More fundamentally, it strips states, local institutions, and citizens of control over their own information. This erosion of data sovereignty is directly at odds with SDG 16, which emphasizes peace, justice, and strong, accountable institutions. Unsurprisingly, many governments are now investing in sovereign AI initiatives, as seen in countries like Singapore and Malaysia, seeking to prevent critical data from falling entirely under external corporate control.
Perhaps the gravest concern, however, is accountability. Centralized AI systems are often opaque. Proprietary models, trained on undisclosed data and tuned with undisclosed methods, operate as “black boxes.” When such systems are used to guide decisions on aid distribution, social protection targeting, migration control, or natural disaster response—decisions that affect millions—who is ultimately responsible for errors and harms? The lack of transparency makes independent auditing extremely difficult and ethically indefensible in high-stakes development contexts. In practice, this opacity threatens progress across all 17 SDGs by undermining trust and accountability.
The crisis confronting AI and global development is not primarily one of technological capability. The underlying algorithms, computing power, and data-processing techniques already exist. What is missing is a governance and architectural model that aligns AI with the principles at the heart of sustainable development: inclusion, sovereignty, accountability, and shared benefit. Moving away from corporate-centric, centralized infrastructures toward decentralized ones is not a matter of ideology; it is a practical requirement.
Decentralized AI offers a path forward. Built on foundations such as federated learning and blockchain-based coordination, it distributes both the training process and the control over data, while retaining the ability to learn from diverse contexts. Rather than funneling raw data into a single corporate repository, decentralized AI allows models to be trained collaboratively across multiple locations, devices, and institutions.
One example of this new architecture is the SDG Blockchain Accelerator Programme, led by the UNDP and supported by partners including Blockchain for Good Alliance, Stellar, FLock.io, and EMURGO Labs. This initiative is exploring how decentralized AI and distributed ledger technologies can be harnessed to empower communities in the Global South instead of turning them into passive data sources. Importantly, it treats local institutions as co-creators of AI solutions, not just as end-users of imported technologies.
Federated learning is central to this reorientation. In a federated setup, a shared model is sent to many decentralized devices or servers—such as hospitals, schools, or government ministries—where it is trained locally on sensitive data that never leaves the premises. Only the model updates, not the raw data, are shared back and aggregated. Over time, the collective model improves by learning from many local contexts, while individuals and institutions retain control over their information.
In the Latin America and Caribbean region, for instance, pilot projects are exploring federated learning for public health. Hospitals can use local patient data to enhance diagnostic models for diseases prevalent in their communities, then contribute anonymized model updates to a regional network. The resulting system is more accurate across diverse populations without any single entity hoarding the underlying medical records. This directly supports SDG 3 on good health and well-being while respecting privacy and sovereignty.
Similar approaches can transform agriculture and climate resilience. Farmers’ cooperatives and agricultural agencies can train local models on soil conditions, crop performance, and microclimate data. These models, refined through federated learning, can then help predict pest outbreaks, optimize fertilizer use, and guide climate-adaptive planting decisions across multiple regions. Here, decentralized AI strengthens SDG 2 on zero hunger and SDG 13 on climate action by tailoring insights to local ecosystems instead of imposing one-size-fits-all recommendations.
Financial inclusion is another domain where decentralized AI can make a decisive impact. Microfinance institutions and community banks in low-income regions often lack high-quality risk models tailored to their clients. Federated approaches allow these institutions to collectively train credit-scoring models on their own transactional and repayment data while keeping that data on local servers. The outcome is more inclusive and context-aware financial tools that expand access to credit, especially for women, informal workers, and smallholder farmers, in line with SDG 1 on poverty reduction and SDG 5 on gender equality.
Blockchain or other distributed ledger technologies complement federated learning by providing transparent coordination and incentive mechanisms. They can record model contributions, track data usage rights, and enforce access policies in a tamper-resistant way. For development actors, this means they can verify which entities contributed to a model, under what terms, and how decisions are being made. This level of auditability is crucial for preserving trust in AI-driven systems, especially where public funds and humanitarian outcomes are at stake.
Critically, decentralization is not only a technical configuration; it is also a shift in power relations. When communities, local governments, and smaller institutions participate directly in model training and governance, they gain a seat at the table in shaping how AI is used and what objectives it serves. This stands in contrast to the prevailing model, where solutions are often “parachuted” in from afar, with limited understanding of local languages, norms, and political dynamics.
Of course, decentralized AI is not a silver bullet. It raises its own challenges. Training distributed models across low-resource environments requires reliable connectivity and sufficient computing capacity. Ensuring robust privacy protections, even when data remains local, demands strong cryptographic and governance safeguards. Designing incentive systems so that contributions from low-income regions are fairly valued requires careful thought. But these are engineering and design problems that can be addressed; they are not structural barriers.
For decentralized AI to fulfill its potential for global development, several priorities must be addressed:
First, infrastructure investment must be redirected. Development finance should not solely fund the adoption of external AI tools, but also support local computing, connectivity, and capacity building that enable participation in decentralized networks. Without this foundation, federated learning and similar approaches remain theoretical.
Second, regulatory frameworks should recognize and protect data sovereignty while encouraging cross-border collaboration. Clear rules on data ownership, consent, and access are essential so that communities can engage in decentralized AI on terms that advance their interests rather than compromise them.
Third, open standards and interoperable protocols are necessary to prevent a new wave of “walled gardens,” this time in decentralized form. If different networks cannot communicate or share models, the global benefits of diverse data and perspectives will be lost. Development agencies, governments, and technologists must converge on shared technical and governance norms.
Fourth, capacity building must be treated as a core pillar, not an add-on. Training local data scientists, policymakers, civil society leaders, and public servants to understand and govern decentralized AI is as important as the technology itself. Otherwise, the narrative may change while control remains concentrated in the hands of a few technical actors.
Finally, accountability mechanisms must be designed into decentralized systems from the start. Transparent logs of model updates, robust mechanisms for contesting harmful decisions, and clear lines of responsibility among participating institutions are essential. Decentralization does not absolve anyone of responsibility; if done well, it makes responsibility traceable and shared.
The stakes could not be higher. If AI continues to evolve along a centralized, corporate path, it will likely deepen existing global divides: between those who produce AI and those who merely consume it, between data-rich and data-poor societies, between regions that shape standards and those forced to adapt to them. The promise of AI as a tool for human development would remain largely rhetorical.
Conversely, if the global development community embraces decentralized AI architectures rooted in inclusion, sovereignty, and accountability, AI can become a genuine public good. It can help countries achieve the Sustainable Development Goals not by replacing local expertise, but by amplifying it; not by extracting value from marginalized communities, but by returning value to them.
The choice is not whether AI will shape the future of development—it already is—but whether its architecture will perpetuate inequality or enable shared progress. Embracing decentralized AI is not just a technical preference; it is an ethical and political imperative for any serious effort to build a more just and sustainable world.