Artificial Intelligence as a Tool for Addressing Real-World Challenges

Artificial intelligence (AI), machine learning (ML), and data science are increasingly proposed as ways to extend scarce expertise, interpret complex data, and improve the timing and targeting of services in low- and middle-income countries and other resource-constrained communities. This paper synthesizes recent peer-reviewed studies and institutional guidance across education, healthcare, humanitarian assistance, disaster response, agriculture, and public information management. Evidence shows that carefully bounded systems can perform useful tasks: offline crop-disease screening can assist extension workers; AI-supported diabetic-retinopathy screening can expand diagnostic capacity; machine-learning forecasts can support flood warnings; and data-driven poverty maps or mobile-phone signals can inform social-assistance targeting. Yet predictive accuracy is not equivalent to public benefit. Benefits depend on representative data, local validation, affordable and resilient infrastructure, accessible interfaces, trusted institutions, and human capacity to act on outputs. Comparative evidence is strongest for specific, measurable tasks and substantially weaker for long-term population outcomes and scalable implementation. Major risks include exclusion of people poorly represented in data, proxy-label bias, privacy and surveillance harms, automation overreach, weak redress, and dependence on external platforms. The paper argues for locally led, task-specific, human-accountable AI, evaluated against non-AI alternatives and real service outcomes. It concludes with a research agenda centered on data stewardship, context-sensitive evaluation, accessible deployment, and equitable governance.