Is artificial intelligence the future for economic development? Earlier this month, a group of World Bank staff, academic researchers, and technology company representatives convened at a conference in San Francisco to discuss new advances in artificial intelligence. One of the takeaways for Bank staff was how AI technologies might be useful for Bank operations and clients. Below you’ll find a full round-up of all the papers and research-in-progress that was presented. All slides that were shared publicly are linked here, as well as papers or other relevant sites.
It’s amazing to see what technology can do these days! Satellites provide daily images of almost every location on earth, and computers can be trained to process massive amounts of data generated from them to produce insightful analysis/information. This is just one of the demonstrations of artificial intelligence (AI). AI can go beyond just reading images captured from space, it can help improve lives overall.
For urban governance, machine learning and AI are increasingly used to provide near real-time analysis of how cities change in practice – for example, through the conversion of green areas into built-up structures. By teaching computers what to look for in satellite images, rapidly expanding sources of satellite data (public and commercial), together with machine learning algorithms, can be leveraged to quickly reveal how actual city development aligns with planning and zoning or which communities are most prone to flooding. This provides insights beyond the basic satellite snapshots and time-lapse visualizations that can now be readily generated for any areas of interest.
But the barriers to applying these technologies can still seem daunting for many cities around the world. It’s not always clear how exactly to analyze this massive amount of satellite data, nor how to get access to it.
Last Thursday I attended a conference on AI and Development organized by CEGA, DIME, and the World Bank’s Big Data groups (website, where they will also add video). This followed a World Bank policy research talk last week by Olivier Dupriez on “Machine Learning and the Future of Poverty Prediction” (video, slides). These events highlighted a lot of fast-emerging work, which I thought, given this blog’s focus, I would try to summarize through the lens of thinking about how it might help us in designing development interventions and impact evaluations.
A typical impact evaluation works with a sample S to give them a treatment Treat, and is interested in estimating something like:
Y(i,t) = b(i,t)*Treat(i,t) +D’X(i,t) for units i in the sample S
We can think of machine learning and artificial intelligence as possibly affecting every term in this expression:
Development work is getting more technologically sophisticated by the day. The World Bank’s Information and Technology Solutions (ITS) department recently started an Artificial Intelligence (AI) Initiative. At the launch event, we explored the role of AI in development and what it might mean for the work that we do here at the Bank. In short: AI is already here, international organizations have an important role to play, and we need to invest in our skills and expertise.
AI is already being incorporated into development projects
A growing family of Artificial Intelligence techniques are being employed in development. Using machine learning for classification and prediction tasks is becoming as routine as running regressions. Our team recently launched a data science competition on poverty prediction and has been evaluating the performance of different machine learning algorithms. This includes the use of automated machine learning where the machine itself helps to select and tune models in a way a data scientist ordinarily would.
Business plan competitions have increasingly become one policy option used to identify and support high-growth potential businesses. For example, the World Bank has helped design and support these programs in a number of sub-Saharan African countries, including Côte d’Ivoire, Gabon, Guinea-Bissau, Kenya, Nigeria, Rwanda, Senegal, Somalia, South Sudan, Tanzania, and Uganda. These competitions often attract large numbers of applications, raising the question of how do you identify which business owners are most likely to succeed?
In a recent working paper, Dario Sansone and I compare three different approaches to answering this question, in the context of Nigeria’s YouWiN! program. Nigerians aged 18 to 40 could apply with either a new or existing business. The first year of this program attracted almost 24,000 applications, and the third year over 100,000 applications. After a preliminary screening and scoring, the top 6,000 were invited to a 4-day business plan training workshop, and then could submit business plans, with 1,200 winners each chosen to receive an average of US$50,000 each. We use data from the first year of this program, together with follow-up surveys over three years, to determine how well different approaches would do in predicting which entrants will have the most successful businesses.
Video: Artificial intelligence for the SDGs (International Telecommunication Union)
Along with my colleagues on the ICT sector team of the World Bank, I firmly believe that ICTs can play a critical role in supporting development. But I am also aware that professionals on other sector teams may not necessarily share the same enthusiasm.
Typically, there are two arguments against ICTs for development. First, to properly reap the benefits of ICTs, countries need to be equipped with basic communication and other digital service delivery infrastructure, which remains a challenge for many of our low-income clients. Second, we need to be mindful of the growing divide between digital-ready groups vs. the rest of the population, and how it may exacerbate broader socio-economic inequality.
These concerns certainly apply to artificial intelligence (AI), which has recently re-emerged as an exciting frontier of technological innovation. In a nutshell, artificial intelligence is intelligence exhibited by machines. Unlike the several “AI winters” of the past decades, AI technologies really seem to be taking off this time. This may be promising news, but it challenges us to more clearly validate the vision of ICT for development, while incorporating the potential impact of AI.
It is probably too early to figure out whether AI will be blessing or a curse for international development… or perhaps this type of binary framing may not be the best approach. Rather than providing a definite answer, I’d like to share some thoughts on what AI means for ICT and development.
Methods that use satellite data and machine learning present a good peek into how Big Data and new analytical methods will change how we measure poverty. I am not a poverty specialist, so I am wondering if these data and techniques can help in how we estimate job growth.
How does political context shape education reforms and their success? Lessons from the Development Progress project
Achieving Sustainable Development Goal 4 – ‘Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all’ – is one of the most important and challenging tasks in international development. In order to fulfil it, we require a better understanding of why progress and the impact of interventions varies so widely by context. One striking gap in our knowledge here is a lack of analysis as to how education systems interact with political contexts that they operate in. This report addresses this gap by drawing on evidence from eight education-focused country case studies conducted by ODI’s Development Progress project and applying political settlements analysis to explore how political context can shape opportunities and barriers for achieving progress in education access and learning outcomes.
Combining satellite imagery and machine learning to predict poverty
Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains. Data imagery of the report is available on the project website.
These are some of the views and reports relevant to our readers that caught our attention this week.
Global Governance Monitor
The Internet has revolutionized communication and radically altered the conduct of business, politics, and personal lives. Information is now widely available and shared through instant message, email, and social media. Businesses can operate internationally with virtually no delay, enabling previously unimaginable opportunities such as providing medical advice across oceans. Moreover, the embedding of sensors, processors, and monitors in everyday products links the physical and virtual worlds, expanding vast streams of data and creating new markets. The Internet has also altered the relationship between governments and societies. Low-cost, nearly ubiquitous communication platforms allow citizens to mobilize and build transnational networks. The speed of communication can make governments more accountable, and open-data initiatives enable the participation of nongovernmental organizations and increased transparency. Though the technology has facilitated unprecedented economic growth, increased access to information, and delivered innovative solutions to historic challenges, the expansion of the Internet has also brought challenges and vulnerabilities.
The 2016 Brookings Financial and Digital Inclusion Project Report, Advancing equitable financial ecosystems
The 2016 Brookings Financial and Digital Inclusion Project (FDIP) evaluates access to and usage of affordable financial services by underserved people across 26 geographically, politically, and economically diverse countries. The 2016 report assesses these countries’ financial inclusion ecosystems based on four dimensions of financial inclusion: country commitment, mobile capacity, regulatory environment, and adoption of selected traditional and digital financial services. The 2016 report builds upon the first annual FDIP report, published in August 2015. The 2016 report analyzes key changes in the global financial inclusion landscape over the previous year, broadens its scope by adding five new countries to the study, and provides recommendations aimed at advancing financial inclusion among marginalized groups, such as women, migrants, refugees, and youth.