Artificial Intelligence for Supporting Low- and Middle-Income Countries by Collecting Local Data from Population Surveys (Extended Abstract)
Funding needs to be provided in order for close partnerships to happen, as well as in making citizen data collecting efforts more effective with artificial intelligence 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. The future of official data is less certain if the US pulls back, but funding for official data started to Stabilize after the Pandemic.
The AidData Research Lab at William & Mary, a university in Williamsburg, Virginia, has an example of an artificial intelligence tool that can be fast but also include a larger portion of the population than a household survey can. AI might also help researchers to evaluate how well programmes meet their objectives and demonstrate how investments in areas such as health, agriculture, education and infrastructure pay off — or not. The World Bank recognizes this value and has been developing advanced AI tools to try to forecast food crises and violent conflicts, and to pull insights from large swathes of data gathered after an aid intervention. In its report, Poverty, Prosperity, and Planet, it stated that it was to focus on machine learning and artificial intelligence models to close data gaps.
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 afraid that deportation of immigrants could be bad, and so are reluctant to participate. But unless their needs are identified, it is difficult to help them.
Could Artificial Intelligence offer a solution? The answer is yes, but with some caveat. The Togo example shows how communities are helped by combining knowledge of geographical areas with more individual data from mobile phones. It is a great example of how some artificial intelligence tools work with household-level data. Community scientists collect data about their area, which is a relatively untapped source for such information. This idea deserves more attention and more funding.
Thanks to technologies such as cellphones and 4G there has been an explosion of people gathering and analyzing their own social and environmental data. Volunteers gather data on litter along the coastline and give this knowledge to their country’s official statistics.
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 can sustain itself. 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. The UN believes citizen science and citizen data can help improve the representation of hard-to-reach populations.
The Story of London: Towards a Numerical Definition of Poverty using Artificial Intelligence and Human Dynamical Modeling
In order to maximize benefits and reduce risks, you have to use artificial intelligence in a certain way. This is especially important when it comes to using AI that involves people who are vulnerable or living in poverty. They have to make their lives better, not expose them to any harms.
AI allowed Lawson to leapfrog the conventional hurdles of using old and incomplete data to quickly make the most of her limited budget. It is both an approach that is getting both interest and controversy according to a computer scientist who works on Novissi.
However flawed AI might be, though, current systems of evaluating poverty are just as abysmal, says BenYishay. “The baseline isn’t perfect data. It’s actually very crappy data,” he says.
British social reformer Charles Booth undertook an early effort to quantify poverty from 1886 to 1903 when he criss-crossed London’s cobblestones collecting data on people’s incomes and social class. He created a colour-coded map of the city and reported his findings in a treatise titled Life and Labour of the People in London. The Poverty: A Study of Town Life book was published in 1901 by English sociologist Seebohm Rowntree and his team. The team calculated poverty on the basis of the ability to meet a person’s “physical efficiency”, or their minimal nutritional requirements. A sample minimal diet could include boiled bacon, potatoes, skimmed milk and bread.
The dollar-per-day approach is easy to convey, said Dean Jolliffe, an economist at the World Bank. How much a person spends is just one of many aspects of poverty. An economist and an Episcopal priest are advocates for a more nuanced definition of poverty. “I want to know how many poor people lack a house, how many poor people have a kid out of school, so I can actually respond in very tangible, direct ways,” says Alkire, who is the director of the Oxford Poverty and Human Development Initiative at the University of Oxford, UK.
Alkire wanted a way to capture the effects poverty has on people. Just because someone has enough money to buy food doesn’t mean they have enough for medical care or school fees, says Alkire. The Multidimensional Poverty index (MPI) was worked on by Alkire and James Foster of George Washington University in Washington DC. A total of ten indicators, including nutrition, school attendance, access to drinking water, and what a household uses for cooking fuel, are included in the assessment of a unified measure of poverty.
Marshall Burke was familiar with the data collection process while he was a PhD student. To learn about farming and agriculture practices in East Africa, Burke travelled to Kenya and Uganda, where he spent months talking to farmers and walking their fields. When Burke was setting up the Environmental Change and Human Outcomes Lab at Cardinal University, he wondered if the computer revolution could offer better approaches.