Imagine building an AI highway in the Global South. If we erect too many toll gates and surveillance checkpoints at the start, local startups may never get on the road. But if we allow everyone to race ahead without any signs, speed limits, or guardrails, we are inviting dangerous accidents and social backlash.
This is the delicate balance many developing nations now face: how do we regulate AI in a way that protects people and values without choking innovation at its roots? In countries struggling with financial inclusion, weak education systems, overwhelmed healthcare, inefficient agriculture, and persistent poverty and corruption, AI holds transformative potential. From enabling micro-credit access in remote villages, to detecting fraud in real-time, personalizing education content, or optimizing fertilizer use for small-scale farmers—AI could help leapfrog development barriers.
But overly restrictive regulations—borrowed wholesale from Global North contexts—could suffocate these hopes before they materialize. If regulation is too heavy-handed, only large corporations with deep pockets will be able to comply. This would effectively push local startups out of the ecosystem, stifling grassroots innovation and leaving local problems unsolved. Worse, the solutions that do get deployed may be ill-suited to the realities of the Global South—designed for different markets, cultures, and infrastructures.
Furthermore, excessive regulation risks missing a golden opportunity: the surge of global interest and investment in AI. Developing countries stand to gain significantly from becoming part of this wave, but only if the ecosystem is made open and attractive to both local talent and international collaboration.
At the same time, a total lack of oversight is not the answer either. Without clear ethical standards and risk mitigation, AI can lead to serious harm—from technical failures to large-scale social consequences like job displacement, widening inequality, and erosion of public trust. We must strike a balance between openness and protection.
What we need is intelligent, adaptive regulation—a framework that is responsive to both the promise and the peril of AI.
This also requires a shift in mindset. AI should be seen not as a replacement for human workers, but as a tool to augment human skills. The goal should not be automation for the sake of efficiency alone, but the creation of solutions that truly address the pressing needs of developing countries—from inclusive education to accessible healthcare and improved public services. When aligned with local realities, AI can empower communities rather than displace them.
In fact, we are already witnessing promising examples from the Global South that prove this is possible:
In Zambia, Netagrow Technologies is helping small-scale farmers battle climate shocks and poor yields through AI-powered diagnostics. Their tools use image recognition to detect plant diseases, offer localized crop and irrigation recommendations, predict harvest outcomes, and even connect farmers to real-time market prices. This AI solution is not about replacing the farmer—it’s about empowering them to grow more, earn more, and adapt faster.
In India, Qure.ai and Vionix Biosciences have revolutionized healthcare access. Qure.ai uses AI to analyze medical scans in seconds, enabling faster diagnoses in rural clinics with few specialists. Vionix Biosciences supports early detection of health risks using AI-driven thermal imaging. Together, they show how AI can bridge human gaps in overburdened health systems without pushing professionals aside.
In Latin America, CENAPRED in Mexico and UNGRD in Colombia are using AI for disaster management. From early warnings for floods and landslides to predictive models that guide evacuation and aid, these tools save lives by offering clarity in crisis. Local data, weather inputs, and satellite feeds are integrated to tailor solutions to each geography’s risk profile.
In Indonesia, Gojek and Grab have harnessed AI to transform transportation and logistics. Facing massive traffic congestion and fragmented delivery infrastructure across the archipelago, these platforms deploy AI for route optimization and demand prediction. Algorithms analyze real-time traffic data to determine the most efficient paths, while dynamic pricing and driver allocation systems anticipate customer needs and position services strategically. The result: faster, more affordable service for users, better earnings for drivers, and reduced traffic burden for cities.
These are not luxury innovations. They are survival tools designed with context in mind.
I call this approach the FAST framework. It stands for Flexible, Accountable, Safe, and Testable.
Flexible, because we need to lower the entry barriers for grassroots innovation. Not every AI initiative should be treated as if it were launching nuclear codes. Let local developers experiment, iterate, and solve community problems without facing the same regulatory hurdles that apply to Big Tech.
Accountable, because freedom without responsibility leads to exploitation. All AI systems, no matter how small, must be transparent, auditable, and open to public scrutiny. Innovation thrives in daylight.
Safe, because there must be non-negotiable ethical guardrails. These include prohibitions against algorithmic discrimination, predatory surveillance, or the misuse of personal data. Safety must be built-in, not added on as an afterthought.
Testable, because policy cannot rely on theory alone. Governments must create real, functional regulatory sandboxes where new tools can be trialed, evaluated, and improved in collaboration with stakeholders.
This isn’t just abstract theory. China has shown how innovation can flourish when regulation enables rather than obstructs. Conversely, the European Union has demonstrated that global trust and market stability often stem from embedding ethics from the beginning.
Too often, the Global South is forced to choose between mimicking Silicon Valley’s hyper-acceleration or Brussels’ compliance-heavy approach. But these are not our only options. We can design something more fitting to our own context—a third way that values agility without sacrificing accountability.
Smart regulation is not a pause button. It is a compass. It helps nations in the Global South navigate rapid technological shifts while safeguarding their people and sovereignty.
Now is the time for South-South collaboration—for governments, technologists, academics, and communities to come together and build governance models that work for us. Ethical models without being elitist. Dynamic without being reckless.
But collaboration must not stop there. We must also engage in meaningful dialogue with the Global North. Many foundational AI technologies are developed there, and the Global North remains a primary source of investment, infrastructure, and regulatory influence. Suppose we are to shape an ecosystem that works for the Global South. In that case, we must be at the table—not just as users of imported technologies, but as equal contributors to global norms, protocols, and innovations.
This dialogue is essential not only to ensure ethical interoperability and fair access but also to influence the direction of investment toward context-appropriate solutions. When South and North co-create with mutual respect, we unlock a path toward a more inclusive digital future—one where no region is left behind.
The road ahead is ours to build. Let us regulate not out of fear, but with foresight.
Let us innovate not alone, but in solidarity.
Let us not ask whether the Global South can shape AI, but when we will lead together.
