Raphael ThysOptimists hope it will ease grave shortages of human capital
image: Cristina Spano
Jan 25th 2024|Dakar, Nairobi and São Paulo
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TWENTY-FIVE years ago your correspondent hired a cellphone in Congo. Each day, it cost what a typical local made in several months. The handset was as heavy as a half-brick and only somewhat more useful. Practically no one else in Congo had one, bar cabinet ministers and tycoons, so there were not many people to call. In those days, mobile phones had made no detectable difference to most people’s lives in the world’s poorest countries.
Today, many farmers in Congo have phones: the number of connections has grown 5,000-fold as the population has doubled. Mobile devices have transformed lives throughout the developing world, especially as more and more of them are hooked up to the internet (see chart 1). The 4bn people who live in low or lower-middle income countries have vastly more access to information, chat daily to far-off friends and use their phones like bank cards even when they don’t have bank accounts.
image: The Economist
Could artificial intelligence (ai) bring similarly dramatic changes? There are three main reasons for optimism. First, the technology is improving fast. Second, it has the potential to spread fast, too. As usually happens with new technologies, rich countries will benefit first. But if the high cost of training AI models falls, the expense of providing the technology to the poor could be minimal. They will not need a new device, just the smartphones that many of them already own.
The third reason is that developing countries have gaping shortages of skilled workers: there are nowhere near enough teachers, doctors, engineers or managers. AI could ease this shortfall, not by replacing existing workers, but by helping them become more productive, argues Daniel Björkegren of Columbia University, which in turn could raise the general level of health and education. Although AI may also eliminate some jobs, the IMF predicts that labour markets in poorer countries will be less disrupted than those in rich ones. Another tantalising possibility is that AI could help provide fine-grained, up-to-date data about poor places, and so assist in all manner of development work.
image: The Economist
Start with education. A typical sub-Saharan pupil spends six years in school but retains only three years’ worth of learning, Wolfgang Lutz of the Wittgenstein Centre in Vienna estimated in 2015. A typical Japanese student spends 14 years in classes and absorbs 16 years’ worth of education. Using a different methodology, the World Bank also finds that education is spectacularly worse in poor countries than in rich ones (see chart 2).
Tonee Ndungu, an entrepreneur in Kenya, thinks AI could help bridge this gap. He has developed two apps that he hopes to launch this year. One, called Somanasi (“Learn with me”), is for children. It allows pupils to ask a talking chatbot questions related to the Kenyan school curriculum. The Economist asked, “How do I work out a percentage from a fraction?” The chatbot offered a step-by-step worked example.
Machine learning
A chatbot can give undivided attention to each child, at any time of day, and never gets tired (so long as your phone is charged). It can also be adapted to local cultures. “I never saw an apple till I was 30,” says Mr Ndungu. “So we say ‘A is for animal.’” The service can be tailored to different learning styles, too. It might illustrate division by telling children to break a pencil in half and then again. Depending on how different pupils respond, the AI can figure out whether this approach works, and fine-tune the way it interacts with them. Some kids want more numbers; some like stories. The chatbot adapts.
It cannot yet mark homework. But Mr Ndungu’s firm, Kytabu, offers an app for teachers, too, called Hodari (“Brave”). It lightens their workload by crafting step-by-step lesson plans. It helps track what pupils understand, by getting each one to answer questions on a smartphone. (One phone per classroom is enough, he says.)
As far as The Economist could tell from playing with them in a café with good Wi-Fi, the two apps work well. But the proof will come—and bugs will be fixed—when more people use them in classrooms and homes. They will be given away to begin with; Mr Ndungu hopes eventually to charge for add-ons. The more children are enrolled, the cheaper it will be to provide the service. If half a million were to join, Mr Ndungu predicts the cost per child would fall from $3.50 a month (not including the phone) to about 15 cents.
Chat GPA
Many entrepreneurs are pursuing similar projects, often using open-source models developed in rich countries, sometimes with help from charities like the Gates Foundation. The cost of getting AI to learn new languages appears low. It is already being used to write children’s books in tongues previously too obscure for commercial publishers to bother with.
The need is glaring. Developing countries have too few teachers, many of whom have not mastered the curriculum. A study in 2015 (using data going back to 2007) found that four-fifths of grade six maths teachers in South Africa did not understand the concepts they were supposed to teach. Nearly 90% of ten-year-olds in sub-Saharan Africa cannot read a simple text.
Dr Björkegren points to recent studies suggesting that big gains are possible even with basic tech. One analysed an approach under which schools hire modestly qualified teachers and give them detailed “scripts” for lessons, delivered via tablet computers. Michael Kremer, a Nobel-prize-winning economist, and others studied 10,000 pupils taught this way in Kenya, at schools run by Bridge International Academies, a chain of cheap private schools. They found that after two years on average Bridge students had mastered nearly an extra year’s worth of the curriculum, compared with pupils enrolled in normal schools. Another study in India found that personalised computerised instruction was especially helpful for pupils who were far behind.
Using AI in health care is riskier. If an educational chatbot misfires, a pupil might flunk a test; if a medical one hallucinates, a patient could die. Nonetheless, optimists see great potential. Some AI-powered medical kit is already widely used in rich countries and is starting to be adopted in poorer ones. Examples include handheld ultrasound devices that can interpret scans, and a system for spotting tuberculosis on chest X-rays. Accurate ai translation could also make it easier for patients and health-care workers in the global south to tap into the world’s medical knowledge.
Even imperfect AI tools may improve health-care systems in the developing world, whose failures cause more than 8m deaths a year, by one estimate. In a study of nine poor and middle-income countries by Todd Lewis of Harvard and others, 2,000 recently graduated primary health-care workers were observed dealing with visitors to clinics. They performed the correct, essential tasks required by clinical guidelines only about half the time.
For people in remote areas, even a substandard clinic may be too far away or too costly. Many rely on traditional medicine, much of which is useless or harmful. South African folk healers sometimes cut patients to rub in toxic powder suffused with mercury, for example. AI tools need not be infallible to be better than that.
A team at the University of São Paulo is training an AI to answer health-related questions. The aim is to give a tool to primary-health workers in Brazil, who sometimes have little training. They are using a database of clinical guidelines from Brazil’s health ministry, rather than the whole internet, which is full of voodoo health tips. Before the ai can be widely deployed, it must be tested, tweaked and tested again. Currently, when you ask precise, technical questions, such as “Is Ivermectin effective in preventing covid-19?”, its success rate is “so, so high”, says Francisco Barbosa, one of the team. The trouble comes when you ask it something vague, as humans often do. If you say, “I’ve fallen in the street. How can I get to a pharmacy?”, then the AI, which may not know where you are, might give terrible advice.
The AI will have to improve and its users will have to learn how to get the best out of it, says Mr Barbosa. He is confident that this will happen: “It’s a cliché [to say it], but it’s changing everything.” Equipping a new hospital costs millions of dollars. Training a new doctor takes years. If AI helps cheap primary-care workers treat more patients successfully, so that they do not need to go to a hospital, Brazil can keep its population healthier without spending more.
Brazil has one doctor for every 467 people; Kenya has one for every 4,425. AI could help, says Daphne Ngunjiri of Access Afya, a Kenyan firm that runs mDaktari, a virtual health-care platform with 29,000 clients. For a small monthly fee, they can ask for advice when they feel unwell.
Bard to handle
For a test group of 380 users, mDaktari has added an AI-powered chatbot to the system. It records their queries, prompts them for more information and presents that information, along with a suggested response, to a clinician, often a nurse. The clinician reads it and, if the advice is sound, approves it and sends it back to the customer, perhaps referring her to a pharmacy or a clinic. Thus, a human is in the loop, to guard against errors, but the AI does the time-consuming gathering of information about symptoms, enabling the nurse to deal with more patients. If necessary, the nurse can call the patient. For embarrassing ailments such as sexually transmitted diseases, some patients prefer talking to a chatbot. It never judges them.
Virginia, a client from a Nairobi slum whose family subsists on casual labour and backyard vegetables, says mDaktari is simple and helpful. One time she felt sick, consulted the app, and was steered to drugs that cleared up what turned out to be a urinary-tract infection. “I can even contact a [nurse] through my phone and get [an] answer,” she says.
Several firms are testing AI-enhanced medical devices to see how well they work in poor areas. Philips, a Dutch firm, has a pilot programme in Kenya for a handheld ultrasound with an AI add-on that can interpret the images it spits out. This helps solve a common problem: lots of pregnant mothers and not enough people with the expertise to read scans.
Sadiki Jira is a midwife at a rural health facility in Kenya that serves nearly 30,000 people but has no doctor. He recalls a pregnant patient a couple of years ago whose baby had died in the womb. She had not realised for several weeks and had only sought help when she started haemorrhaging. Mr Jira referred her to a hospital, but it was too late: she died.
Mr Jira now uses an AI-powered scanner. Any midwife can, with minimal training, swipe a Philips device over a pregnant woman’s stomach. The AI reveals such vital information as the fetus’s gestational age, whether it is in the breech position and whether there is adequate amniotic fluid. “It’s easy to use,” says Mr Jira.
Philips is planning to offer the device and AI together for $1 or $2 a day in poor countries. The biggest obstacles to its rollout are regulatory, says Matthijs Groot Wassink of Philips. Will governments allow midwives to handle a process that previously required someone more qualified? What will happen in places like India, where regulations are especially tight for fear that people will use ultrasound to identify and abort baby girls?
Poorer places collect poorer data. Forty-nine countries have gone more than 15 years since their most recent agricultural census; 13 have not conducted a population census in that period. Official numbers, when they exist, tend to flatter the government. For example, a study compared official estimates of how much maize was being grown on small farms in Ethiopia, Malawi and Nigeria with the results of painstaking (but rare) household surveys. The official numbers were much rosier.
Satellite imagery and machine-learning could improve the quality and timeliness of data in developing countries, argue Marshall Burke of Stanford University and his co-authors in a recent paper in Science. Roughly 2.5bn people live in households that depend on tiny plots of land. Until recently the output of such farms was hard to measure: satellite pictures were not sharp enough and data drawn from them were too hard to interpret. But by setting AI to work on new high-resolution images of vegetation, Dr Burke and David Lobell, also of Stanford, were able to measure crop yields as accurately as surveys do, but faster and more cheaply. This could allow frequent, detailed analysis of farming practices. How much fertiliser is needed on this hillside? Which seeds work best in that valley? Such knowledge could transform rural livelihoods, the authors predict.
So could better weather forecasts. Atmo, an American firm, says its AI-powered weather forecasts are as much as 100 times more detailed and twice as accurate as a conventional meteorological bulletin, because the AI processes data so much faster. It is also cheap. “A dirty secret of meteorology…is that there are vast inequalities,” says Alex Levy, Atmo’s boss. Forecasts are less detailed or reliable in poor countries. ”The places [with] the most extreme weather also have the worst forecasts, [so] they are most likely to be surprised and unable to prepare adequately.” Atmo’s service is being used in Uganda and may soon be deployed in the Philippines.
Population counts in poor countries are rare, because they are costly, and prone to manipulation. In Nigeria the money each state gets from the central government is tied to its population. This gives states an incentive to fiddle. In 1991, on a census form with space for up to nine members per household, some states reported exactly nine members in every one. When the results of the census of 2006 were published, Bola Tinubu, the governor of Lagos, angrily claimed that its population was double the official tally. Nigeria has not held another census since. A new president—Mr Tinubu, as it happens—promises one in 2024.
image: Cristina Spano
AI can generate more frequent, detailed estimates of how many people live where—and how well-off they are. Lights at night are often used as a proxy for economic buzz. Neal Jean of Stanford and others took day and night images of slums in Africa and trained a convolutional neural network (a form of machine learning) to predict from daytime images how much light there would be at night. In other words, AI learnt to recognise the kinds of buildings, infrastructure and other markers that tend to go with economic activity. It was able to predict 55-75% of the variation in assets between households.
Such information could help governments and charities assess better the effects of efforts to help the needy; it could also help companies understand markets. Researchers are avidly trying out such techniques, but governments have been slow to adopt them, laments Dr Burke. He attributes this in part to “the potential benefits to some policymakers of not having certain outcomes be measured”.
AI could also help people deal with the red tape that throttles productivity in so many poor countries. Registering a property takes 200 times longer in Haiti than in wealthy Qatar, according to the World Bank. Suppose an AI, which is immune to boredom, were able to fill in the forms accurately enough to spare humans the chore? In September India launched a chatbot that lets illiterate farmers pose oral queries about applications for financial aid. Some 500,000 tried it on the first day.
Deep minefield
AI poses risks to poor countries, too. They are generally less democratic than rich ones, so many governments will adopt AI surveillance tools, pioneered by China, to monitor and control their people. They are less stable, so incendiary deepfakes may be more likely to warp politics or spark violence. Underfunded and inexpert regulators may struggle to impose proper guardrails against potential abuses.
And there are big obstacles to deploying AI in the developing world. Access to the internet will have to improve. Some countries will benefit faster than others. India has 790m mobile broadband users, plus a universal digital identity system and a super-cheap, real-time payments system, note Nandan Nilekani and Tanuj Bhojwani, two tech bosses, in Finance & Development. This, they argue, “puts it in a favourable position to be the world’s most extensive user of AI by the end of this decade”.
Enormous uncertainty remains about how powerful the technology will eventually prove. But the potential upside is big enough to warrant a tremor of excitement. In the best-case scenario, AI could help make whole populations healthier, better educated and better informed. In time, that could make them a lot less poor. ■
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This article appeared in the Briefing section of the print edition under the headline "Chatbots for the bottom four billion"
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