Tag: Joshua Blumenstock

A Year of Solutions Science and Scholarship at the Blum Center

Shankar Sastry

What is the role of the university in the wider world? What is the role of scholarship in an era of vast digitally enabled knowledge?

These are two questions we at the Blum Center keep forefront in our minds, as we pursue forward-looking curricula and solutions scholarship related to development. During the 2018-2019 academic year, we sought to practice what we preach by holding interdisciplinary faculty salons on large development questions, both to bolster what we teach and how we can learn from one another.

The faculty salon series was kicked off by Michael Nacht, UC Berkeley’s Thomas and Alison Schneider Chair in Public Policy. The former Assistant Secretary of Defense for Global Strategic Affairs explored the nexus of national security, diplomacy, and development—and gave a sober assessment of what that nexus might produce under the Trump administration. Michael concluded that development in low-income countries will not come out of the strategic interactions of the U.S.’s economic and foreign policy positions but likely will be spurred by the for-profit sector through advances in agricultural technology, artificial intelligence, and bioengineering.

In November, Robotics Professor Ken Goldberg and Business Professor Laura Tyson, Blum Center Chair of the Trustees and Business Professor, debated the effects of automation and machine learning on employment across nations and economies. Ken, who believes automation will both eliminate and create new jobs, proposed a “multiplicity movement” to foster uniquely human skills that AI and robots cannot replicate: creativity, curiosity, imagination, empathy, human communication, diversity, and innovation. He recommended the U.S. reinforce creative and social skills in high schools and universities, so that Americans are in a position to leverage machines with varying levels of automation alongside diverse groups of people to amplify intelligence and spark problem solving.

Laura pointed out that the substitution of intelligent machines for low-cost, low-productivity workers poses the greatest challenge in Africa, where by 2050 the continent’s youth population is estimated to increase by 50 percent to 945 million. She said we must focus our attention on how African countries will fare in global trade and global supply chains, when the availability of comparatively cheap labor is no longer a competitive advantage. She advocated that nations develop comprehensive educational and development strategies that support the livelihoods of their citizens—and that share the benefits of intelligent machines broadly.

In December, Bioengineering Professor and Blum Center Chief Technologist Dan Fletcher presented on his own solutions science related to the London Declaration of Tropical Diseases. Nearly a decade ago, the declaration brought together more than 80 global organizations to control, eliminate, or eradicate at least ten of the diseases by 2020. Progress has been made on some of the diseases, but they still affect nearly one billion people, even though major pharmaceutical companies have pledged to contribute the treatment drugs. The main problem now, explained Dan, is a health information gap—both in terms of who has the diseases and where they are located. His mobile microscopy device CellScope, developed over a decade plus, can fill this gap because it both identifies the infected through testing and provides effective treatment and monitoring, even in the most remote areas. Dan has proven his technological intervention in several major papers, and is now on mission to fund the implementation of this life-saving innovation.

In early 2019, we welcomed Joshua Blumenstock from School of Information, to the faculty salon. Blumenstock, director of the Data-Intensive Development Lab, cautioned that even though the application of machine learning to monitor and alleviate poverty has become a much discussed aspiration, new digital methods may serve more as a complement than a replacement to traditional approaches, especially in the area of economic assessment. However, he did point out that satellite imagery is becoming a key source for development research because it reveals basic physical infrastructure and quality of life trends. In his own research, Joshua has shown that by leveraging machine learning to analyze satellite data, we can draw conclusions about certain aspects of the quality of life with nearly the same accuracy as traditional, multimillion-dollar field surveys.

Technological interventions are never clear cut. This was illustrated in the April Faculty Salon by Professors Isha Ray of the Energy and Resources Group and Alison Post of the Political Science Department. They shared their analysis of the effects of the UC Berkeley-incubated social enterprise NextDrop, which designed a mobile phone intervention to alert Indian households via text when to expect water supply. Isha and Alison’s two-year study found the SMS service failed to have its intended time-saving effect due to a combination of oversights by NextDrop in terms of water service provision, mobile phone ownership, and other information gaps. “It is absolutely essential to understand the role of human intermediaries and how drastically the conditions and results of an intervention can change from one setting to the next,” said Isha.

In May, we discussed Kenya’s rural electrification efforts, studied by Ted Miguel, Oxfam Professor of Environmental and Resource Economics, and Catherine Wolfram, Cora Jane Flood Professor of Business Administration. Although Kenya has received massive foreign assistance to achieve universal energy access, the economic benefits of rural electrification in the world’s poorest places are not straightforward. Ted and Catherine’s research team conducted a randomized control trial to study the effects of electricity connections in 150 Kenyan communities, and found no meaningful medium-run impacts on economic, health, and educational outcomes. The reason? Even when heavily subsidized, the cost of connecting was a significant burden for many households whose average annual cash earnings were $205. In addition, rural Kenyans had no money to buy time-saving, productivity-enhancing appliances like refrigerators or computers. 

“Power isn’t like water,” said Ted. “It isn’t like turning on the tap and getting something that improves your livelihood. Power requires you to connect to an appliance. But if you are too poor to buy something to connect to power, the hypothesized effects are not there.”

The last faculty salon of the academic year was led by Dan Kammen, Distinguished Professor of Energy, and Solomon Hsiang, Chancellor’s Professor of Public Policy, who engaged in a wide ranging conversation with interdisciplinary faculty on the economics, politics, and development impacts of climate change. Kammen has spent much of his two-decade career at UC Berkeley focusing on renewable energy research, with a focus on the role of developing economies. He underscored that in Kenya, which has a robust mobile money system, off-grid solar-generated energy is becoming the norm in many rural areas. This illustrates, he said, that around the globe—from California (which will reach its 2025 zero net carbon emission targets ahead of time) to Morocco (which is the only country meeting Paris climate accord goals)—solar, wind, and other renewable energy sources are proving to be implementable and economically viable.

The problem, of course, is that the transition away from fossil fuels to renewables is not happening quickly enough. However, Solomon, whose Global Policy Laboratory researches what we need to know to design global policy, said public interest in climate change modeling  has increased dramatically over the last two years and the conversation among governments is now how detrimental will be the social cost of global warming, particularly for Southern Hemisphere countries. “This is where the role of information and academic research becomes economically powerful,” he argued.

The Blum Center Faculty Salons will continue in the fall. Stay tuned for more news about how faculty across the disciplines can collaborate on solutions science and scholarship for global public benefit.

Shankar Sastry is Faculty Director of the Blum Center for Developing Economies and NEC Distinguished Professor of Electrical Engineering and Computer Sciences at UC Berkeley. 

Joshua Blumenstock: The Knowns and Unknowns of Big Data and Poverty Alleviation

In international development circles, the application of machine learning to monitor and alleviate poverty has become a much discussed aspiration. However, Joshua Blumenstock, assistant professor at the UC Berkeley School of Information and director of the Data-Intensive Development Lab, cautioned at a recent Blum Center Faculty Salon that unknowns abound and new digital methods may serve more as a complement than a replacement to traditional approaches, especially in the area of economic assessment.

At the salon, Blumenstock highlighted two ways big data is altering the field of international development: first, in measuring quality of life and welfare in low-income countries; and second, in offering financial inclusion applications for poor populations. His colleague Moritz Hardt, assistant professor of electrical engineering and computer science, provided a lead response, drawing from his decade of research on fairness and machine learning. Together, they highlighted that over the past five years big data sets—from mobile phone companies, satellite imagery, social media platforms, and international development organizations—paired with advances in machine learning technology, have generated fascinating and controversial work.  

“Over less than a decade we have experienced a global explosion of data, bringing us to this fairly nascent intersection of big data and poverty alleviation efforts,” said Blumenstock. “With the mass availability of large-scale data sets, we now have access to new sources of data on previously remote, low-resource settings.”

A key contributor to these new data sets is the stunning rise in cell phone adoption. According to the World Bank, more households in developing countries own a mobile phone than have access to electricity or clean water, and nearly 70 percent of the bottom fifth of the population in developing countries owns a mobile phone (note: not a smartphone). An increase in satellite and remote sensing data has also contributed to the data explosion. The combination of these data sources, with machine learning, means that data can be synthesized and applied in new ways.

Blumenstock said that satellite imagery in particular is becoming a key source for development research because it reveals basic physical infrastructure and quality of life trends, such as roof material, road quality, and land plot size. This information can help researchers estimate the basic traits of a town, including average household wealth and population density. Blumenstock is currently conducting research with Facebook to provide a publicly available map of micro-regional estimates of wealth and poverty.

“Leveraging machine learning to analyze these forms of data, we can draw conclusions about certain aspects of quality of life with nearly the same accuracy as traditional, multi-million dollar field surveys,” Blumenstock explained.

Given the time and cost savings, international multilateral organizations like the World Bank and United Nations are eager to start applying these big data applications. Likewise, many governments in developing countries are eager to bypass traditional data collection methods in favor of machine learning-assisted data analysis because of the large time and monetary costs of national census surveys.

Blumenstock is hopeful that by supplementing traditional poverty indices with high-frequency estimates based on satellite and digital data, development practitioners can have low-cost options for impact evaluations and project monitoring. He said this data-plus-machine-learning approach could help open up major innovations in three areas: 1) targeting specific populations for program implementation; 2) monitoring and mitigating the effects of natural disasters, health epidemics, and migration patterns by allowing, for example, aid workers to deliver needed resources to hard-hit areas; and 3) enabling different approaches to impact evaluation, specifically randomized control trials, which can costs millions of dollars.

Financial inclusion was the other area Blumenstock highlighted as potentially benefiting from algorithm-based decision making. He pointed out that globally 1.7 billion people lack a bank account, half of whom are women in poor, remote regions—yet about two-thirds of this population have access to a mobile phone. Companies like M-Pesa, launched in 2007 in Kenya, are engaged in wide-scale mobile phone-based money transfering, financing, and micro-financing services. As a result, there has been a surge in “digital credit” banking led by the private sector in low-income countries, which is increasing financial inclusion for populations without formal credit.

Using data to analyze phone use patterns, some banks and intermediary financial technology (fintech) companies are testing ways to develop alternative digital credit scores to provide uncollateralized loans to the unbanked. By aggregating digital trace data that includes Internet searches, emails composition, even browser and smartphone choices, and then using machine learning to assess the data, banks can formulate digital credit scores that predict who is most likely to default on a loan. One of the largest entitles to use this approach is a Kenyan digital savings and loan product called M-Shwari, which is built on M-PESA and run by the Commercial Bank of Africa and the mobile network operator Safaricom. Using M-Shwari, customers who lack a bank and credit history can take out loans. Beyond increasing accessibility to loans, digital credit also has the potential to dramatically reduce transaction costs and provide immediate disbursement.

Providing loans to previously unbanked populations can stimulate critical economic growth. Yet Blumenstock was quick to point out that the concept of digital credit scoring and it’s rapid growth across developing economies raises several concerns. First, most of these loans are short-term with very high interest rates, which can indebt customers. Second, leaning too heavily on algorithms to churn out credit scores can create a variety of biases.

Blumenstock recently visited Kenya to gain greater insight into the mobile banking process, where digital loans have quickly risen in popularity. According to a 2018 study led by FSD-Kenya, more than one in four Kenyans have taken out a digital loan over the past five years, comprising an estimated 6 million Kenyan borrowers. At the time of the study, more than half of these digital borrowers had at least one outstanding loan, and 14 percent had digital loans from multiple banks. Among the long-term implications to digital credit-based loans are credit bubbles, over-indebtedness, and the overall impact on social welfare.

“There’s a lot of allure to using AI to leapfrog traditional methods, from digital currency to data collection,” said Blumenstock. “But it creates a silver bullet fallacy problem. We’re still grossly unaware of its impacts and what exacerbating issues it could lead to.”

Lead discussant Moritz Hardt spoke on the limitations of machine learning, particularly in relation to gender and race biases, and their corresponding consequences to everything from credit scores to healthcare predictions to providing child services to decisions in the criminal justice system.

“It’s not easy to define discrimination in algorithmic decision-making processes,” said Hardt. “We are at a sobering stage right now; people are becoming aware of the limitations and questioning possible structural issues.”

Hardt provided an example of how risk assessment algorithms are used as a predictive tool to determine which individuals are at high risk for missing their court date following an arrest. If deemed as pretrial “high risk” by the algorithm, an arrested individual is held in jail until their court date, with often dire consequences for their income and family circumstances. Such predictive algorithms are similarly used to inform criminal justice officials decisions on how high to set the bail, sentencing, and who gets early release.

“What is often neglected in designing algorithms are the structural and complex socio-cultural challenges unique to each person,” Hardt said.

Blumenstock responded that “we need to endogenize social sciences into machine learning,” warning that taking off-the-shelf algorithms for ad targeting and plopping them into poverty targeting would have obvious negative results.

“Off-the-shelf tools typically assume that the social processes being modeled are static,” he said. “But these processes are inherently dynamic, changing over time and over subpopulations. The appropriate use of machine learning in such contexts requires a more nuanced understanding of the people who are being targeted, and what assumptions might be reasonable or, more often, totally implausible.”

—Lisa Bauer