Combine AI with citizens’ science to fight poverty

Of the myriad of artificial intelligence applications (AI), its use in humanitarian assistance is underestimated. In 2020, during the COVID-19 pandemic, the Togo government used AI tools to identify tens of thousands of households that needed money to buy food, like Nature Reports in a new feature this week. As a general rule, the potential beneficiaries of these payments would be identified when they are asking for social protection schemes or through household surveys on income and expenses. But these investigations were not possible during the pandemic, and the authorities had to find other ways to help those who need them. The researchers used automatic learning to paint through satellite imaging of low -income areas and combined this knowledge with data from mobile phones to find eligible recipients, who then received regular payment via their phones. The use of AI tools in this way changed the situation for the country.

Can AI help poverty? Researchers are testing ways to help the poorest people

Now, with the end pandemic, researchers and decision -makers continue to see how AI methods can be used in poverty reduction. This requires complete and precise data on the state of poverty in households. For example, to be able to help individual families, authorities must know the quality of their accommodation, the diet of their children, their education and if the basic needs of the health and medical families are met. This information is generally obtained from surveys in person. However, researchers saw a drop in response rates when collecting this data.

Missing data

The collection of data -based data can be particularly difficult in low and intermediate income countries (LMIC). Personal surveys are expensive to do and often lack some of the most vulnerable, such as refugees, people living in informal housing or those who earn their lives in the cash economy. Some people hesitate to participate for fear that there may be harmful consequences – expulsion in the case of undocumented migrants, for example. But unless their needs are identified, it is difficult to help them.

Take advantage of the collaborative power of AI and the science of citizens for sustainable development

Could AI offer a solution? The short answer is, yes, although with warnings. The example of Togo shows how AI -centered approaches have helped communities by combining knowledge of the geographic areas of need with more individual mobile phones. This is a good example of how AI tools work well with the granular data in terms of cleaning. Researchers are now established on a relatively unexploited source for such information: the data collected by citizen scientists, also called 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, cities and villages collecting, storing and analyzing their own social and environmental data. In Ghana, for example, Volunteer researchers collect data on marine litter along the coastline And contribute to this knowledge to the official statistics of their country.

Citizen collaboration

Last December, a group of data scientists argued in an article of perspective in Natural sustainability that this data could be used by political decision -makers in collaboration with AI tools (D. Freshisl et al. Nature. 8125–132; 2025). In the play, Dilek Fraisisl, of the International Institute for the analysis of the systems applied to Laxenburg, Austria, and his colleagues require Partnership between AI researchers and citizen scientists.

The authors could push to an open door. International organizations such as the United Nations Statistical Commission, which establishes the standards for measuring official statistics, want more citizen scientists to contribute data, such as the United Nations sustainable development objectives (SDG), the global plan to end poverty and achieve environmental sustainability. Populations that are difficult to reach poorly represented in the stage reports of the SDGsAnd the UN considers the data of the sciences of citizens as a potential solution.

But making such narrow partnerships requires funding, both to support citizen data collection efforts and to get them to the higher level with AI tools. This could be a challenge at a time when the United States, which is the largest national data and statistics fund in the PRFR, withdraws international commitments, especially Leave the World Health Organization And Foreign help galling. The financing of official statistics began to stabilize after the pandemic, but the future will be less certain if the United States withdraws (see “Data Dollar”).

The integration of AI into citizen data has many advantages. On the one hand, it allows communities to appropriate their information, knowing that it is their data that they collect and store, and that the data will not be held by a third party. The statistics of precise and well -organized citizens could also improve the quality of AI tools, which often perpetuate biases or inaccuracies found in their training data. The use of AI also has the potential to accelerate the analysis of this data.

AI must be deployed in a way that maximizes advantages and reduces or reduces risks. This is particularly important when it comes to using AI which involves vulnerable people or who live in poverty. AI must improve their lives and not expose them to additional or different damage.

Citizens’ data could be the medication that the doctor ordered. All those who participate in this research must be encouraged and research itself must be funded appropriately.