Why AI Matters in the Global South

Introduction:

Artificial Intelligence (AI) is no longer a luxury reserved for tech giants in the Global North. In fact, some of the most promising and impactful applications of AI are emerging from countries in the Global South, where innovation meets urgency. From diagnosing crop diseases to expanding access to education, AI is being leveraged to solve pressing developmental challenges and support sustainable growth.

Yet, these advancements come with critical challenges—data inequality, infrastructure gaps, and systemic bias—that require tailored solutions rooted in collaboration, ethics, and capacity-building.

1. Historical Context: Bridging Technological Disparities

The integration of AI into developing regions must be understood against a historical backdrop marked by inequality in technological access and investment. Countries in the Global South have often depended on partnerships with more technologically advanced nations, relying on international support for infrastructure, training, and research capabilities.

Now, many of these nations are asserting a new digital identity by adopting AI technologies to bridge the gap and create transformative change from within

2. Applications of AI in Key Sectors

Agriculture: Feeding the Future

AI tools such as image recognition and predictive analytics are revolutionizing agriculture. In regions where food insecurity is a persistent issue, AI assists farmers by identifying crop diseases, suggesting planting techniques, and optimizing irrigation practices. The result? Improved yields, reduced waste, and more resilient food systems.

Healthcare: Closing the Access Gap

In underserved areas with a shortage of medical professionals, AI is stepping in as a critical support system. Chatbots assist with symptom analysis, algorithms help diagnose diseases, and remote diagnostics extend healthcare to rural communities. For instance, Ghana’s AI-powered tools for mosquito detection have contributed to malaria prevention, a major health burden in the region.

Education: Personalized, Scalable Learning

With millions of children lacking access to quality education, AI-powered learning platforms are delivering personalized, adaptive lessons to students in remote areas. These tools can help address the global teacher shortage while making learning more inclusive and engaging.

Governance and Financial Inclusion: Smarter Services

AI is enhancing the efficiency of public services, particularly in social protection programs. Togo’s use of AI to identify households most in need of cash transfers is a prime example. AI is also enabling broader access to financial services through credit scoring, fraud detection, and mobile banking.

3. Challenges Hindering AI’s Full Potential in the Global South

Bias and Inequality in Algorithms

AI systems often reflect the biases of the data they are trained on. Without diverse and representative datasets, marginalized communities risk being excluded or misrepresented. For example, facial recognition tools have been shown to be significantly less accurate for people with darker skin tones.

Data Access and Privacy

Large volumes of useful data are often siloed within corporations or governments, and even when accessible, may come with privacy risks. Balancing openness with data protection remains a delicate but necessary pursuit.

Skill Gaps and Talent Shortages

Despite growing enthusiasm for AI, many countries lack sufficient experts to build, maintain, and scale AI systems. Investment in localized AI education and upskilling is essential.

Sustainability and Last-Mile Implementation

NGOs and startups often struggle with maintaining AI systems over time. AI success stories need to move beyond pilots and prototypes, toward long-term integration with local systems and communities.

Infrastructure and Structural Barriers

Digital exclusion, limited internet access, and outdated regulatory frameworks pose ongoing hurdles. Governments must take the lead in promoting inclusive digital infrastructure and fair policies to bridge the gap.

4. Real-World Impact: Case Studies and Innovations

From AI-assisted ultrasounds tailored for Indigenous women in Guatemala to regional pandemic preparedness initiatives like AI4PEP, success stories abound. These examples highlight how AI can be shaped to reflect local needs, values, and cultural contexts.

5. The Path Forward: Strategies for Sustainable Impact

Build Collaborative Frameworks

Cross-regional partnerships between Global North and South stakeholders can foster knowledge sharing and mutual growth. Joint research initiatives, shared AI infrastructure, and open-source platforms are vital.

Invest in Local Capacity

Developing countries should focus on foundational investments in energy, connectivity, and education. Small modular reactors and decentralized compute solutions can power data centers sustainably.

Align with SDGs and Ethics

AI solutions should not only be effective but also equitable. Aligning with Sustainable Development Goals (SDGs) ensures a holistic approach where technological growth supports social justice and economic inclusion.

Adapt Regulatory Frameworks

Policymakers must anticipate ethical dilemmas and data governance challenges. By proactively developing adaptable AI regulations, countries can foster innovation while safeguarding rights and dignity.

Conclusion: A Transformative Opportunity—If We Get It Right

AI holds extraordinary promise for the Global South—not just to catch up with the rest of the world, but to leapfrog into a more inclusive, just, and innovative future. But unlocking this potential requires a commitment to fairness, collaboration, and local empowerment.

As we stand at the intersection of innovation and development, the question is no longer whether AI will shape the future of the Global South—but whether we will shape it ethically, inclusively, and sustainably.