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Last week, at an event on artificial intelligence for economic development, Josh Blumenstock, an assistant professor from the University of California, Berkeley, gave a talk entitled “Fighting Poverty with Data.”
Blumenstock talked about his work gathering mobile phone data from the cell phones of users in Rwanda and then turning that information, such as the number of calls made per day, SMS volume, and international contacts, into interpretable metrics. His research is at the intersection of machine learning and international development, the focus of the event organized by the Center for Effective Global Action and the World Bank at Google’s headquarters in San Francisco. He highlighted how mobile phone metadata can predict poverty, and the implications of that in terms of targeting aid, crisis response, and impact evaluation.
At one point, one of the engineers in the room asked Blumenstock how location data factored into his work. “The problem is that people aren’t using smartphones yet,” he replied, matter-of-factly. While it might come as a surprise to people who have not spent time in developing country contexts, the majority of the people Blumenstock works with have basic phones they use only for calling and texting.
Unless developers and designers focus on the short-term reality as well as the long- term future, they will fail to reach the relatively poor and marginalized segments of the population, leaving those who do not have smartphones even further behind.
Read the full article about designing for dumb phones to fight poverty by Catherine Cheney at Devex International Development.