AI for Global Challenges

Among the most compelling arguments for prioritizing AI development is its potential to address humanity's greatest shared challenges: pandemic disease, climate change, food insecurity, and unequal access to education. These claims deserve to be taken seriously — and examined critically. AI has demonstrated genuine capability in each of these domains. It has also been accompanied by significant hype, uneven benefit distribution, and recurring failures. A rigorous student of AI and society should be able to hold both truths simultaneously.

AI and Global Health

AI's most celebrated contribution to global health is AlphaFold, DeepMind's system for predicting the three-dimensional structure of proteins from their amino acid sequences. Before AlphaFold, determining protein structure experimentally took months to years and cost hundreds of thousands of dollars per protein. AlphaFold 2, released in 2021, predicted structures for virtually all catalogued proteins with accuracy that matched experimental methods — a scientific breakthrough with profound implications for drug discovery, vaccine development, and understanding disease mechanisms. Its database is now freely available to researchers worldwide. Beyond protein structure, AI is being applied to disease surveillance (analyzing social media, electronic health records, and satellite imagery to detect outbreak signals before formal reporting), diagnostic imaging (detecting tuberculosis, diabetic retinopathy, and cancer in chest X-rays and retinal scans), and drug candidate screening (identifying molecules with potential therapeutic value from vast chemical spaces). During the COVID-19 pandemic, AI tools were deployed for vaccine development (messenger RNA design), genomic surveillance of variants (processing millions of genome sequences), and hospital resource optimization. The results were mixed: some applications contributed meaningfully; others were rushed to deployment without adequate validation and performed poorly in real-world conditions. A persistent tension in AI for global health is deployment inequality. The most sophisticated AI diagnostic tools are validated on patient populations in wealthy countries and may perform worse on patients from other populations — different skin tones affect dermatology AI, different disease prevalences affect diagnostic thresholds, different healthcare norms affect what 'normal' looks like. Tools built for wealthy-country healthcare systems may not transfer reliably to low-resource settings without significant adaptation.

AI and Climate Change

Climate change presents both an opportunity and an irony for AI. The opportunity: AI systems are being deployed across the full climate problem, from detection to mitigation to adaptation. The irony: AI systems — particularly the large-scale training of foundation models — are themselves significant consumers of energy and water. On the detection side, AI is used to analyze satellite imagery for deforestation monitoring (Global Forest Watch uses ML to detect illegal deforestation within days of its occurrence), to model glacier retreat, and to improve climate simulation by parameterizing processes too fine-grained for traditional models. On the mitigation side, AI is applied to grid optimization (Google's DeepMind reduced cooling energy in its data centers by 40% using reinforcement learning), to building energy efficiency, to the design of next-generation solar cells and battery chemistries, and to precision agriculture that reduces fertilizer runoff. On the adaptation side, AI-driven early warning systems for floods, droughts, and extreme heat events are being deployed in vulnerable regions. Google's Flood Hub provides flood forecasts up to seven days in advance for river basins in Africa, Asia, and Latin America — regions with limited existing forecasting infrastructure. The energy footprint of AI is a genuine concern. Training GPT-4 reportedly produced carbon emissions roughly equivalent to 300 transatlantic flights. Inference — running the model at scale for billions of users — multiplies this. The AI industry has made commitments to renewable energy, but its absolute energy consumption is growing faster than its renewable fraction, meaning net emissions are still rising.

AI and Global Education

UNESCO estimates that 300 million children worldwide lack access to quality education. AI tutoring systems offer a potential path to personalized, accessible instruction at scale — but the gap between promise and reality remains large. The most rigorous evidence for AI's educational benefit comes from Mindspark, an AI-based tutoring program tested in India. A randomized controlled trial found that students using Mindspark for 45 days improved substantially more in mathematics and Hindi than a control group — gains equivalent to several months of additional learning. Similar results have been found for Khan Academy's AI-assisted learning and for AI tutoring systems in reading. However, AI educational tools designed for well-resourced contexts often fail in low-resource settings. Systems that assume reliable electricity, broadband connectivity, and device ownership reach only a fraction of the 300 million children who most need support. Language barriers, curriculum mismatch, and cultural context further limit transferability. A more cautionary example: in 2020, during school closures from COVID-19, many countries rapidly deployed AI-based automated grading systems to replace disrupted examinations. In the UK, an algorithm that used school historical performance to predict A-level grades systematically downgraded students from lower-income schools and upgraded students from elite schools — perpetuating and encoding existing educational inequality. The system was abandoned after widespread protests, but the incident illustrated how AI systems can amplify rather than address structural disparities.