Combine AI with citizen science to fight poverty

Of the myriad applications of artificial intelligence (AI), its use in humanitarian assistance is underappreciated. In 2020, during the COVID-19 pandemic, Togo’s government used AI tools to identify tens of thousands of households that needed money to buy food, as Nature reports in a News Feature this week. Typically, potential recipients of such payments would be identified when they apply for welfare schemes, or through household surveys of income and expenditure. But such surveys were not possible during the pandemic, and the authorities needed to find alternative means to help those in need. Researchers used machine learning to comb through satellite imagery of low-income areas and combined that knowledge with data from mobile-phone networks to find eligible recipients, who then received a regular payment through their phones. Using AI tools in this way was a game-changer for the country.

Can AI help beat poverty? Researchers test ways to aid the poorest people

Now, with the pandemic over, researchers and policymakers are continuing to see how AI methods can be used in poverty alleviation. This needs comprehensive and accurate data on the state of poverty in households. For example, to be able to help individual families, authorities need to know about the quality of their housing, their children’s diets, their education and whether families’ basic health and medical needs are being met. This information is typically obtained from in-person surveys. However, researchers have seen a fall in response rates when collecting these data.

Missing data

Gathering survey-based data can be especially challenging in low- and middle-income countries (LMICs). In-person surveys are costly to do and often miss some of the most vulnerable, such as refugees, people living in informal housing or those who earn a living in the cash economy. Some people are reluctant to participate out of fear that there could be harmful consequences — deportation in the case of undocumented migrants, for instance. But unless their needs are identified, it is difficult to help them.

Leveraging the collaborative power of AI and citizen science for sustainable development

Could AI offer a solution? The short answer is, yes, although with caveats. The Togo example shows how AI-informed approaches helped communities by combining knowledge of geographical areas of need with more-individual data from mobile phones. It’s a good example of how AI tools work well with granular, household-level data. Researchers are now homing in on a relatively untapped source for such information: data collected by citizen scientists, also known as community scientists. This idea deserves more attention and more funding.

Thanks to technologies such as smartphones, Wi-Fi and 4G, there has been an explosion of people in cities, towns and villages collecting, storing and analysing their own social and environmental data. In Ghana, for example, volunteer researchers are collecting data on marine litter along the coastline and contributing this knowledge to their country’s official statistics.

Citizen collaboration

Last December, a group of data scientists argued in a Perspective article in Nature Sustainability that these data could be used by policymakers in conjunction with AI tools (D. Fraisl et al. Nature Sustain. 8, 125–132; 2025). In the piece, Dilek Fraisl, of the International Institute for Applied Systems Analysis in Laxenburg, Austria, and colleagues call for a partnership between AI researchers and citizen scientists.

The authors could be pushing at an open door. International organizations such as the United Nations Statistical Commission, which sets the standards for measuring official statistics, want more citizen scientists to contribute data, such as for the UN Sustainable Development Goals (SDGs), the world’s plan to end poverty and achieve environmental sustainability. Hard-to-reach populations remain poorly represented in SDG progress reports, and the UN sees citizen science and citizen data as a potential solution.

But making such close partnerships happen needs funding, both in supporting citizen-data collecting efforts, and in taking them to the next level with AI tools. This could be a challenge at a time when the United States, which is the largest national funder of data and statistics in LMICs, is withdrawing from international commitments, including exiting the World Health Organization and freezing foreign aid. Funding for official statistics started to stabilize after the pandemic, but the future will be less certain if the United States pulls back (see ‘Data dollars’).

Integrating AI with citizen data has many benefits. For one, it enables communities to take ownership of their information, knowing that it is their data that they are collecting and storing, and that the data will not be held by a third party. Accurate and well-curated citizen statistics could also improve the quality of AI tools, which often perpetuate bias or inaccuracies found in their training data. The use of AI also has the potential to speed up analysis of those data.

AI has to be deployed in a way that maximizes benefits and mitigates or reduces risks. This is especially important when it comes to using AI that involves people who are vulnerable or living in poverty. AI has to make their lives better and not expose them to further or different harms.

Citizen-science data might just be the medicine that the doctor ordered. All those who participate in this research must be encouraged and the research itself needs to be appropriately funded.