AI | marginalized challenges

AI is often praised as a game-changer that can revolutionize healthcare, education, and more. But its potential is often overstated, especially in regions that lack the basics like reliable electricity or clean water. Introducing advanced AI tech in areas without basic infrastructure could end up worsening existing inequalities instead of fostering inclusive growth.

For many marginalized communities, consistent electricity is a luxury, and clean water is a daily challenge. In these situations, talking about AI-powered education or health solutions can feel disconnected from reality. How can farmers benefit from machine learning to improve crop yields if they can't even count on electricity for irrigation, let alone a stable internet connection? Without essential infrastructure, the practical benefits of AI are out of reach, further alienating these communities from the progress celebrated in global development forums.

Digital literacy is another huge barrier, and many underprivileged areas lack it. If we implement AI solutions without bridging this knowledge gap, we risk creating a dependency on external actors, undermining the independence of local communities. Instead of empowering people, AI can end up reinforcing a top-down approach that leaves the very people it's supposed to help sidelined, unable to engage with the technology affecting their lives.

Resources spent on AI might be better used to develop the infrastructure needed to make future tech adoption possible. Communities need electricity, clean water, sanitation, and education before they need AI. Until those basics are in place, AI risks being a flashy but ultimately superficial solution—something that looks good on policy papers but doesn’t change the daily reality for marginalized populations. The focus on high-tech interventions instead of foundational needs points to a worrying disconnect between international development goals and what people on the ground actually need.

This disconnect is also evident in the types of problems AI is designed to solve. AI solutions often assume a certain level of connectivity and infrastructure that just doesn't exist in underserved areas. For instance, AI-driven health diagnostics might be excellent in cities with reliable internet and power, but those assumptions crumble in rural areas where even treating a simple fever can be life-threatening due to lack of healthcare access. Introducing AI-powered health apps without addressing basic healthcare infrastructure first is like putting the cart before the horse. It could actually widen the digital divide, allowing some to benefit from technology while others fall further behind.

AI's reliance on large datasets adds another layer of complexity. Data collection is often sparse, outdated, or inaccurate in marginalised areas. This leads to biases in AI algorithms that can reinforce harmful stereotypes or make wrong predictions. If AI tools are based on unreliable data, they won't just be ineffective—they could actively harm, making misguided recommendations that don’t reflect what people truly need. This can result in policy decisions that overlook the real experiences of marginalized communities, making inequalities even worse.

There are also ethical concerns around data collection. AI often requires personal data, and in marginalized areas, people may not fully understand how their data is being used or even have a choice in the matter. This lack of informed consent can lead to exploitation, with data being collected without proper safeguards or respect for privacy. Marginalized communities risk becoming mere data points in a global AI experiment, with little regard for their autonomy or rights.

Instead of rushing to implement AI, development efforts should focus on building the infrastructure and human capacity that can lay the foundation for future tech adoption. Clean water, stable electricity, education, and digital literacy should come first. Once those are in place, communities will be in a much better position to benefit from AI in ways that are equitable and truly empowering.

Community-driven approaches are also crucial to making sure AI solutions are genuinely beneficial. Engaging local stakeholders in developing and implementing AI projects can help create a sense of ownership and ensure that solutions are tailored to actual community needs. Integrating traditional knowledge and local context into AI technologies can make them more relevant and effective. Development should be about co-creating technology that aligns with the aspirations and realities of the people it aims to serve, not imposing high-tech solutions from the outside.

Moving forward, we must fundamentally rethink how AI fits into international development. AI can definitely help uplift marginalized communities, but only if the groundwork is laid to make it effective. That means focusing on infrastructure, education, and local involvement. Without these, AI will just be another buzzword—a promise of progress that never actually materializes for those who need it most.