About the AI x Global Health and Wellbeing team
The GHW team makes grants across scientific research, policy advocacy, and global health and development to serve our mission of helping others as much as we can with the resources available to us. We prioritize grants with the potential to improve health outcomes and economic wellbeing, with most of our work focused on people living in LMICs.
The AIxGHW team is new, and it sits within our Cause Prioritization team. Our job is to set the strategy for CG’s work at this intersection, conduct research to figure out which approaches are most promising, and make up to $100 million in cost-effective grants in the next year. Our grantmaking falls into two broad categories:
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Mitigating noncatastrophic but serious risks to global health and wellbeing from transformative AI.
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Ensuring that new AI-enabled solutions to improve health and economic outcomes globally get built and scaled effectively.
Here are some illustrative projects we would be excited to fund:
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Seed funding for a new policy organization that speeds up drug development. A small organization focused on what major global health funders and multilaterals should be doing now to prepare for accelerated medical breakthroughs.
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Technical assistance program for governments redesigning their safety nets for AI-driven job losses. Fund a small team embedded within an LMIC government to help redesign cash transfer targeting, eligibility, and financing mechanisms that are robust to labor market disruption.
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Building a data access layer. Set up proposals for data-sharing frameworks, consent, and privacy protections to enable cheaper and faster impact evaluation of global health interventions, including RCTs and monitoring.
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AI-interpreted diagnostics to narrow healthcare infrastructure gaps. Phone or handheld tools that read chest X-rays for tuberculosis, retinal images for diabetic retinopathy, or cervical images so that under-resourced health workers can access specialist-grade input.
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Frontline public-worker support. Legal aid drafting and case triage for overloaded courts; translation and summarization of case files.
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Open public datasets, especially in specific domains and in local languages. Labeled text/speech for under-resourced languages, particularly in high-importance sectors (e.g. Yoruba × agriculture, Swahili × health, etc.).
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LMIC-specific AI benchmarks. Build benchmarks that reflect local epidemiology, languages, and contexts and track frontier model performance on them over time.
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Usage studies. Baseline descriptive or causal studies that establish the value of out-of-the-box models, how people in LMICs use them, and the impact of those uses. This information might motivate AI usage training programs in LMICs.
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New “AI for good” accelerators and incubators. These programs might draw on local talent familiar with the contexts for deployments, e.g. recruiting clinicians or health workers to deploy local AI for Health tools.
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Climate-sensitive disease early warning. Forecasting outbreaks of climate-linked diseases (e.g. malaria, dengue, cholera) from environmental and ecological signals, so that bednets, vaccines, drugs, and water treatment resources reach people before a surge rather than after.
This list of potential projects is far from exhaustive, but it illustrates the range of work we’re considering as well as our uncertainty about which interventions will be most impactful.
Across the org, we’re significantly scaling our grantmaking and rapidly growing our team to support it, in part because we expect to see large increases in philanthropic funding flowing into the fields where we work in the coming years. Accelerating now lets us fill cost-effective gaps today, and helps lay the groundwork for new donors to step in as well.
Growth at this pace means the work is fast, ambiguous, and often messy: we need to make big decisions, pivot quickly, and make trade-offs. We think this is a uniquely high-leverage moment, and we’re looking for people who find that energizing.