Artificial intelligence is becoming omnipresent and has the potential to improve every aspect of human life. Whilst rapid technological progression does risk exacerbating inequality, there are also reasons to be optimistic. Technologists are already harnessing AI’s data-mining ability in the fight against what the UN calls today’s greatest global challenge: poverty.
Thanks to artificial intelligence, Netflix makes suggestions based on your viewing history. Amazon recommends items based on your browsing and purchases. Facebook presents you with targeted ads after crawling your online profile. AI-enabled smartphone apps respond to your queries and can quite often even interpret your needs without taking any instruction.
Elisabeth Mason is a founding director of the Stanford Poverty & Technology Lab, an incubator for technology-based solutions to poverty. She says that technology “puts us in a better position to solve issues that we’ve never been able to solve”. But equally, given the many causes of poverty — including lack of affordable local food, inequality, low levels of education and skills, natural disasters, and epidemics — technology is not a “silver bullet” either.
Some researchers are using AI to pinpoint the regions that are most in need. Other scientists are integrating AI into research designed to improve agriculture, possibly giving the world’s poorest farmers a way to elevate their financial status. AI is also an effective tool for increasing access to information and boosting education and literacy.
Using AI to locate poverty
Recognizing the causes of poverty is key in being able to assess how to tackle the problem using technologies. ‘Fixing’ poverty demands that we first know where it is manifest. In many countries, household surveys and census data can be used to identify poor neighborhoods. But more often than not this information isn’t readily available, especially in the developing nations of Africa and South Asia where gathering this data is prohibitively expensive.
Technologists previously made an attempt to leverage the constant stream of photographs that are taken by satellites — particularly those that reveal the planet at night — to get a better feel for global economic activity. They showed that — perhaps unsurprisingly — regions that glow brightly tend to be wealthy. The problem with this approach is that it cannot distinguish between places of near-poverty and those of absolute poverty.
In 2018 Stanford University economist Marshall Burke instead turned to daytime satellite images and used artificial intelligence to fill in the informational gaps. Instead of curating the data and telling the computer what features to look for, such as bright and dark areas, Burke embraced machine learning so that his AI model could figure it out all by itself.
Burke fed his algorithm night-time and daytime images from Uganda, Tanzania, Nigeria, Malawi, and Rwanda — all of which had household survey data and instructed the algorithm to find features in the daytime imagery that are predictive of places that were lit up at night. The researchers then fed the computer the survey data and instructed it to predict the distribution of poverty throughout the countries. The algorithm could predict poverty 81 percent to 99 percent more accurately than the night-light-only model.
By using this type of AI model policymakers can monitor economic wellbeing in various parts of the world and evaluate the effectiveness of anti-poverty programs. The satellite imagery and AI can also be used in combination to identify areas that require the most urgent aid.
Agriculture & poverty
According to the World Bank, 65 percent of poor working adults make they’re living through agriculture. It is because of this that investing in the agriculture sector is up to four times more effective in reducing poverty than investing in other economic sectors.
Agricultural development is a hugely powerful tool for poverty reduction, and AI can play a vital role in advancing new developments. George Kantor, a robotics expert at Carnegie Mellon University launched ‘FarmView’ to combine AI with robotics to improve the agricultural yield of certain staple crops, in particular sorghum. In developing countries like India, Nigeria, and Ethiopia, this drought-and-heat-tolerant plant is a valuable cereal crop that has huge genetic potential thanks to its more than 40,000 varieties.
To make the perfect crop with the right combination of disease resistance, nutrition, and yield, farmers must selectively combine different crop varieties to create new “children” crops to test. But keeping track of the many different seed strains and their individual attributes makes this process very slow and time-consuming. Robots and AI can speed things up. In Africa, a breeder could be looking at 100 varieties of sorghum a year. Artificial intelligence could help to increase that to 1,000.
In Kantor’s lab, four-wheeled robots drive through a field of sorghum plants, using cameras, laser sensors, and multispectral sensors to measure everything from the size and color of the plant to the nutritional content of its leaves, to signs of disease. The robots use AI to safely navigate by analysing their field of view to differentiate between plants and soil. At the end of the growing season, Kantor and scientists at Clemson University in South Carolina will feed the massive amount of environmental, growth, and genetic data collected from hundreds of sorghum varieties into an AI model.
By parsing the data for hidden patterns, the AI will help the scientists predict the yield of a particular variety based on early-season plant attributes or associate specific desirable traits with genetic markers.
If this initiative is successful, the researchers could conduct similar trials in agriculture-heavy countries like Kenya, giving poor farmers the information that they need to cultivate the most nutritionally packed crop of sorghum possible for their environment — at the highest yield.
Poor education is a major impediment to individuals being able to escape from poverty. Artificial intelligence can play a vital role in deploying educational programs that are relevant to those who are most in need.
Self-guided computer learning programs like Khan Academy — which allows anyone to “learn anything” for free — can help, but there is room for improvement. If developers implemented AI into such programs, the tools could learn from and respond to users, adapting to their specific needs, especially if translated to mobile platforms within poor communities.
In the future, we could soon see AI-powered chatbots being able to stand in as teachers for students who do not have access to other forms of schooling. As long as there is access to a computer and Internet connection (which is itself a huge challenge given the digital divide within poor countries) an ‘AI-teacher’ could guide pupils through a syllabus using real-time analytics and machine learning to assess the education and learning level and skills of individual pupils. This could entirely eradicate the ‘cost’ entry barrier to education.
Regardless of what artificial intelligence tools are developed, they must be implemented in the communities where they can do the most good. The majority of technology investments don’t go to the poor and are instead to improve upper-middle-class people to wealthy standards across the world. To unlock the promise of technology to combat poverty, more investment must go into technologies that are designed to elevate poor communities.