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Updated On: Sunday, 15 July 2018
Development Issues

Machine learning targets development problems

Machine learning is being used to help solve development problems with promising results, say researchers who have produced a roadmap to guide future projects against common pitfalls.

Increasingly popular in rich countries, machine learning is a type of artificial intelligence (AI) in which computers learn — without being explicitly programmed — by finding statistical associations within vast quantities of data.

But using it to solve development problems has been more difficult.

Despite a number of attempts to apply it to tackle poverty, famine or displacement, “we have yet to see successful stories of machine learning truly advancing development”, according to Maria De-Arteaga, of the Machine Learning and Public Policy program at Carnegie Mellon University in Pittsburgh Pennsylvania in the United States. De-Artega spoke at the UNESCO conference Tech4Dev in Switzerland last week (27-29 June), where other researchers presented some projects that show its promise.
Ermon described his team’s success predicting crop productivity in the United States in the months before harvest: their accuracy was comparable to the US Department of Agriculture’s on-the-ground surveys. The model has now been used in Argentina, Brazil and India, he said, and is being extended to Africa. But he admitted there were problems adapting to smaller field sizes and poorer ground data with which to train the system. Ermon’s team also developed a tool that matches survey data with features within satellite images of 30 African countries. It can now predict some indices of development from these data, such as access to electricity, piped water and sewage.

“We are transitioning from an active information search to a supervised information extraction”

Leonardo Milano

“For a few crude measures of infrastructure quality this model seems to work pretty well,” he said. Wesley van der Heijden, a Masters student at Tilburg University in The Netherlands, described attempts to predict hunger in Ethiopia without the need for costly surveys. The computer worked out correlations between satellite, economic and demographic data, on the one hand, and hunger predictions from the Famine Early Warning System, FEWS NET, on the other. The results worked for predicting urgent hunger situations but not for less serious ones, he said. And Leonardo Milano, a senior data scientist at the Geneva-based Internal Displacement Monitoring Centre, said its machine learning system has learned to extract information about internally displaced people from 5,000 media reports daily. This produces smaller, manageable quantities of information that humans can analyse.
De-Arteaga is working with colleagues to collate research on what they call a new research area, Machine Learning for Development (ML4D). She told the conference that the roadmap, to be published in a peer-reviewed journal, defines what it is and who should be involved, outlines how it should be approached and identifies its research challenges, such as data reliability. One aim is to avoid repeating mistakes made in other development fields, she said.



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