Robotics for Global development

Curated on Posted on by Stefaan Verhulst

Report by the Frontier Tech Hub: “Robotics could enable progress on 46% of SDG targets  yet this potential remains largely untapped in low and middle-income countries. 

While technological developments and new-found applications of artificial intelligence (AI) keep captivating significant attention and investments, using robotics to advance the Sustainable Development Goals (SDGs) is consistently overlooked. This is especially true when the focus moves from aerial robotics (drones) to robotic arms, ground robotics, and aquatic robotics. How might these types of robots accelerate global development in the least developed countries? 

We aim to answer this question and inform the UK Foreign, Commonwealth & Development Office’s (FCDO) investment and policy towards robotics in the least developed countries (LDCs). In an emergent space, the UK FCDO has a unique opportunity to position itself as a global leader in leveraging robotics technology to accelerate sustainable development outcomes…(More)”.

From Insights to Action: Amplifying Positive Deviance within Somali Rangelands

Curated on Posted on by Stefaan Verhulst

Article by Basma Albanna, Andreas Pawelke and Hodan Abdullahi: “In every community, some individuals or groups achieve significantly better outcomes than their peers, despite having similar challenges and resources. Finding these so-called positive deviants and working with them to diffuse their practices is referred to as the Positive Deviance approach. The Data-Powered Positive Deviance (DPPD) method follows the same logic as the Positive Deviance approach but leverages existing, non-traditional data sources, in conjunction with traditional data sources to identify and scale the solutions of positive deviants. The UNDP Somalia Accelerator Lab was part of the first cohort of teams that piloted the application of DPPD trying to tackle the rangeland health problem in the West Golis region. In this blog post we’re reflecting on the process we designed and tested to go from the identification and validation of successful practices to helping other communities adopt them.

Uncovering Rangeland Success

Three years ago we embarked on a journey to identify pastoral communities in Somaliland that demonstrated resilience in the face of adversity. Using a mix of traditional and non-traditional data sources, we wanted to explore and learn from communities that managed to have healthy rangelands despite the severe droughts of 2016 and 2017.

We engaged with government officials from various ministries, experts from the University of Hargeisa, international organizations like the FAO and members of agro-pastoral communities to learn more about rangeland health. We then selected the West Golis as our region of interest with a majority pastoral community and relative ease of access. Employing the Soil-Adjusted Vegetation Index (SAVI) and using geospatial and earth observation data allowed us to identify an initial group of potential positive deviants illustrated as green circles in Figure 1 below.

From Insights to Action: Amplifying Positive Deviance within Somali Rangelands
Figure 1: Measuring the vegetation health within 5 km community buffer zones based on SAVI.

Following the identification of potential positive deviants, we engaged with 18 pastoral communities from the Togdheer, Awdal, and Maroodijeex regions to validate whether the positive deviants we found using earth observation data were indeed doing better than the other communities.

The primary objective of the fieldwork was to uncover the existing practices and strategies that could explain the outperformance of positively-deviant communities compared to other communities. The research team identified a range of strategies, including soil and water conservation techniques, locally-produced pesticides, and reseeding practices as summarized in Figure 2.

From Insights to Action
Figure 2: Strategies and practices that emerged from the fieldwork

Data-Powered Positive Deviance is not just about identifying outperformers and their successful practices. The real value lies in the diffusion, adoption and adaptation of these practices by individuals, groups or communities facing similar challenges. For this to succeed, both the positive deviants and those learning about their practices must take ownership and drive the process. Merely presenting the uncommon but successful practices of positive deviants to others will not work. The secret to success is in empowering the community to take charge, overcome challenges, and leverage their own resources and capabilities to effect change…(More)”.

A US-run system alerts the world to famines. It’s gone dark after Trump slashed foreign aid

Curated on Posted on by Stefaan Verhulst

Article by Lauren Kent: “A vital, US-run monitoring system focused on spotting food crises before they turn into famines has gone dark after the Trump administration slashed foreign aid.

The Famine Early Warning Systems Network (FEWS NET) monitors drought, crop production, food prices and other indicators in order to forecast food insecurity in more than 30 countries…Now, its work to prevent hunger in Sudan, South Sudan, Somalia, Yemen, Ethiopia, Afghanistan and many other nations has been stopped amid the Trump administration’s effort to dismantle the US Agency for International Development (USAID).

“These are the most acutely food insecure countries around the globe,” said Tanya Boudreau, the former manager of the project.

Amid the aid freeze, FEWS NET has no funding to pay staff in Washington or those working on the ground. The website is down. And its treasure trove of data that underpinned global analysis on food security – used by researchers around the world – has been pulled offline.

FEWS NET is considered the gold-standard in the sector, and it publishes more frequent updates than other global monitoring efforts. Those frequent reports and projections are key, experts say, because food crises evolve over time, meaning early interventions save lives and save money…The team at the University of Colorado Boulder has built a model to forecast water demand in Kenya, which feeds some data into the FEWS NET project but also relies on FEWS NET data provided by other research teams.

The data is layered and complex. And scientists say pulling the data hosted by the US disrupts other research and famine-prevention work conducted by universities and governments across the globe.

“It compromises our models, and our ability to be able to provide accurate forecasts of ground water use,” Denis Muthike, a Kenyan scientist and assistant research professor at UC Boulder, told CNN, adding: “You cannot talk about food security without water security as well.”

“Imagine that that data is available to regions like Africa and has been utilized for years and years – decades – to help inform divisions that mitigate catastrophic impacts from weather and climate events, and you’re taking that away from the region,” Muthike said. He cautioned that it would take many years to build another monitoring service that could reach the same level…(More)”.

Human Development and the Data Revolution

Curated on Posted on by Stefaan Verhulst

Book edited by Sanna Ojanperä, Eduardo López, and Mark Graham: “…explores the uses of large-scale data in the contexts of development, in particular, what techniques, data sources, and possibilities exist for harnessing large datasets and new online data to address persistent concerns regarding human development, inequality, exclusion, and participation.

Employing a global perspective to explore the latest advances at the intersection of big data analysis and human development, this volume brings together pioneering voices from academia, development practice, civil society organizations, government, and the private sector. With a two-pronged focus on theoretical and practical research on big data and computational approaches in human development, the volume covers such themes as data acquisition, data management, data mining and statistical analysis, network science, visual analytics, and geographic information systems and discusses them in terms of practical applications in development projects and initiatives. Ethical considerations surrounding these topics are visited throughout, highlighting the tradeoffs between benefitting and harming those who are the subjects of these new approaches…(More)”

A Roadmap to Accessing Mobile Network Data for Statistics

Curated on Posted on by Stefaan Verhulst

Guide by Global Partnership for Sustainable Development Data: “… introduces milestones on the path to mobile network data access. While it is aimed at stakeholders in national statistical systems and across national governments in general, the lessons should resonate with others seeking to take this route. The steps in this guide are written in the order in which they should be taken, and some readers who have already embarked on this journey may find they have completed some of these steps. 

This roadmap is meant to be followed in steps, and readers may start, stop, and return to points on the path at any point. 

The path to mobile network data access has three milestones:

  1. Evaluating the opportunity – setting clear goals for the desired impact of data innovation.
  2. Engaging with stakeholders – getting critical stakeholders to support your cause.
  3. Executing collaboration agreements – signing a written agreement among partners…(More)”

The 2026 Aid Transparency Index is canceled. Here’s what it means

Curated on Posted on by Stefaan Verhulst

Article by Gary Forster: “As things stand, we will not be running the 2026 Aid Transparency Index. Not because it isn’t needed. Not because it isn’t effective. But because, in spite of our best efforts, we haven’t been able to secure the funding for it.

This is not a trivial loss. The Aid Transparency Index has been the single most powerful mechanism driving improvements in the quantity and quality of aid data that is published to the International Aid Transparency Initiative, or IATI, Standard. Since 2012, every two years, it has independently assessed and ranked the transparency of the world’s 50 largest aid agencies — organizations responsible for 92% of all spending published in IATI, amounting to $237 billion in 2023 alone.

The index works because it shapes agency behavior. It has encouraged reluctant agencies to start publishing data; motivated those already engaged to improve data quantity and quality; and provided a crucial, independent check on the state of global aid transparency…(More)”.

The disparities and development trajectories of nations in achieving the sustainable development goals

Curated on Posted on by Stefaan Verhulst

Paper by Fengmei Ma, et al: “The Sustainable Development Goals (SDGs) provide a comprehensive framework for societal progress and planetary health. However, it remains unclear whether universal patterns exist in how nations pursue these goals and whether key development areas are being overlooked. Here, we apply the product space methodology, widely used in development economics, to construct an ‘SDG space of nations’. The SDG space models the relative performance and specialization patterns of 166 countries across 96 SDG indicators from 2000 to 2022. Our SDG space reveals a polarized global landscape, characterized by distinct groups of nations, each specializing in specific development indicators. Furthermore, we find that as countries improve their overall SDG scores, they tend to modify their sustainable development trajectories, pursuing different development objectives. Additionally, we identify orphaned SDG indicators — areas where certain country groups remain under-specialized. These patterns, and the SDG space more broadly, provide a high-resolution tool to understand and evaluate the progress and disparities of countries towards achieving the SDGs…(More)”

AI Is Bad News for the Global South

Curated on Posted on by Stefaan Verhulst

Article by Rachel Adams: “…AI’s adoption in developing regions is also limited by its design. AI designed in Silicon Valley on largely English-language data is not often fit for purpose outside of wealthy Western contexts. The productive use of AI requires stable internet access or smartphone technology; in sub-Saharan Africa, only 25 percent of people have reliable internet access, and it is estimated that African women are 32 percent less likely to use mobile internet than their male counterparts.

Generative AI technologies are also predominantly developed using the English language, meaning that the outputs they produce for non-Western users and contexts are oftentimes useless, inaccurate, and biased. Innovators in the global south have to put in at least twice the effort to make their AI applications work for local contexts, often by retraining models on localized datasets and through extensive trial and error practices.

Where AI is designed to generate profit and entertainment only for the already privileged, it will not be effective in addressing the conditions of poverty and in changing the lives of groups that are marginalized from the consumer markets of AI. Without a high level of saturation across major industries, and without the infrastructure in place to enable meaningful access to AI by all people, global south nations are unlikely to see major economic benefits from the technology.

As AI is adopted across industries, human labor is changing. For poorer countries, this is engendering a new race to the bottom where machines are cheaper than humans and the cheap labor that was once offshored to their lands is now being onshored back to wealthy nations. The people most impacted are those with lower education levels and fewer skills, whose jobs can be more easily automated. In short, much of the population in lower- and middle-income countries may be affected, severely impacting the lives of millions of people and threatening the capacity of poorer nations to prosper…(More)”.

Rethinking the Measurement of Resilience for
Food and Nutrition Security

Curated on Posted on by Stefaan Verhulst

Paper by John M. Ulimwengu: “This paper presents a novel framework for assessing resilience in food systems, focusing on three dynamic metrics: return time, magnitude of deviation, and recovery rate. Traditional resilience measures have often relied on static and composite indicators, creating gaps in understanding the complex responses of food systems to shocks. This framework addresses these gaps, providing a more nuanced assessment of resilience in agrifood sectors. It highlights how integrating dynamic metrics enables policymakers to design tailored, sector-specific interventions that enhance resilience. Recognizing the data intensity required for these metrics, the paper indicates how emerging satellite imagery and advancements in artificial intelligence (AI) can make data collection both high-frequency and location-specific, at a fraction of the cost of traditional methods. These technologies facilitate a scalable approach to resilience measurement, enhancing the accuracy, timeliness, and accessibility of resilience data. The paper concludes with recommendations for refining resilience tools and adapting policy frameworks to better respond to the increasing challenges faced by food systems across the world…(More)”.

Impact Inversion

Curated on Posted on by Stefaan Verhulst

Blog by Victor Zhenyi Wang: “The very first project I worked on when I transitioned from commercial data science to development was during the nadir between South Africa’s first two COVID waves. A large international foundation was interested in working with the South African government and a technology non-profit to build an early warning system for COVID. The non-profit operated a WhatsApp based health messaging service that served about 2 million people in South Africa. The platform had run a COVID symptoms questionnaire which the foundation hoped could help the government predict surges in cases.

This kind of data-based “nowcasting” proved a useful tool in a number of other places e.g. some cities in the US. Yet in the context of South Africa, where the National Department of Health was mired in serious capacity constraints, government stakeholders were bearish about the usefulness of such a tool. Nonetheless, since the foundation was interested in funding this project, we went ahead with it anyway. The result was that we pitched this “early warning system” a handful of times to polite public health officials but it was otherwise never used. A classic case of development practitioners rendering problems technical and generating non-solutions that primarily serve the strategic objectives of the funders.

The technology non-profit did however express interest in a different kind of service — what about a language model that helps users answer questions about COVID? The non-profit’s WhatsApp messaging service is menu-based and they thought that a natural language interface could provide a better experience for users by letting them engage with health content on their own terms. Since we had ample funding from the foundation for the early warning system, we decided to pursue the chatbot project.

The project has now spanned to multiple other services run by the same non-profit, including the largest digital health service in South Africa. The project has won multiple grants and partnerships, including with Google, and has spun out into its own open source library. In many ways, in terms of sheer number of lives affected, this is the most impactful project I have had the privilege of supporting in my career in development, and I am deeply grateful to have been part of the team involved bringing it into existence.

Yet the truth is, the “impact” of this class of interventions remain unclear. Even though a large randomized controlled trial was done to assess the impact of the WhatsApp service, such an evaluation only captures the performance of the service on outcome variables determined by the non-profit, not on whether these outcomes are appropriate. It certainly does not tell us whether the service was the best means available to achieve the ultimate goal of improving the lives of those in communities underserved by health services.

This project, and many others that I have worked on as a data scientist in development, uses an implicit framework for impact which I describe as the design-to-impact pipeline. A technology is designed and developed, then its impact is assessed on the world. There is a strong emphasis to reform, to improve the design, development, and deployment of development technologies. Development practitioners have a broad range of techniques to make sure that the process of creation is ethical and responsible — in some sense, legitimate. With the broad adoption of data-based methods of program evaluation, e.g. randomized control trials, we might even make knowledge claims that an intervention truly ought to bring certain benefits to communities in which the intervention is placed. This view imagines that technologies, once this process is completed, is simply unleashed onto the world, and its impact is simply what was assessed ex ante. An industry of monitoring and evaluation surrounds its subsequent deployment; the relative success of interventions depends on the performance of benchmark indicators…(More)”.