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)”.
Rethinking the Measurement of Resilience for
Impact Inversion
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)”.
AI Investment Potential Index: Mapping Global Opportunities for Sustainable Development
Paper by AFD: “…examines the potential of artificial intelligence (AI) investment to drive sustainable development across diverse national contexts. By evaluating critical factors, including AI readiness, social inclusion, human capital, and macroeconomic conditions, we construct a nuanced and comprehensive analysis of the global AI landscape. Employing advanced statistical techniques and machine learning algorithms, we identify nations with significant untapped potential for AI investment.
We introduce the AI Investment Potential Index (AIIPI), a novel instrument designed to guide financial institutions, development banks, and governments in making informed, strategic AI investment decisions. The AIIPI synthesizes metrics of AI readiness with socio-economic indicators to identify and highlight opportunities for fostering inclusive and sustainable growth. The methodological novelty lies in the weight selection process, which combines statistical modeling and also an entropy-based weighting approach. Furthermore, we provide detailed policy implications to support stakeholders in making targeted investments aimed at reducing disparities and advancing equitable technological development…(More)”.
Launching the Data-Powered Positive Deviance Course
Blog by Robin Nowok: “Data-Powered Positive Deviance (DPPD) is a new method that combines the principles of Positive Deviance with the power of digital data and advanced analytics. Positive Deviance is based on the observation that in every community or organization, some individuals achieve significantly better outcomes than their peers, despite having similar challenges and resources. These individuals or groups are referred to as positive deviants.
The DPPD method follows the same logic as the Positive Deviance approach but leverages existing, non-traditional data sources, either instead of or in conjunction with traditional data sources. This allows for the identification of positive deviants on larger geographic and temporal scales. Once identified, we can then uncover the behaviors that lead to their success, enabling others to adopt these practices.
In a world where top-down solutions often fall short, DPPD offers a fresh perspective. It focuses on finding what’s already working within communities, rather than imposing external solutions. This can lead to more sustainable, culturally appropriate, and effective interventions.
Our online course is designed to get you started on your DPPD journey. Through five modules, you’ll gain both theoretical knowledge and practical skills to apply DPPD in your own work…(More)”.
Voice and Access in AI: Global AI Majority Participation in Artificial Intelligence Development and Governance
Paper by Sumaya N. Adan et al: “Artificial intelligence (AI) is rapidly emerging as one of the most transformative technologies in human history, with the potential to profoundly impact all aspects of society globally. However, access to AI and participation in its development and governance is concentrated among a few countries with advanced AI capabilities, while the ‘Global AI Majority’ – defined as the population of countries primarily encompassing Africa, Latin America, South and Southeast Asia, and parts of Eastern Europe – is largely excluded. These regions, while diverse, share common challenges in accessing and influencing advanced AI technologies.
This white paper investigates practical remedies to increase voice in and access to AI governance and capabilities for the Global AI Majority, while addressing the security and commercial concerns of frontier AI states. We examine key barriers facing the Global AI Majority, including limited access to digital and compute infrastructure, power concentration in AI development, Anglocentric data sources, and skewed talent distributions. The paper also explores the dual-use dilemma of AI technologies and how it motivates frontier AI states to implement restrictive policies.
We evaluate a spectrum of AI development initiatives, ranging from domestic model creation to structured access to deployed models, assessing their feasibility for the Global AI Majority. To resolve governance dilemmas, we propose three key approaches: interest alignment, participatory architecture, and safety assurance…(More)”.
Local Systems
Position Paper by USAID: “…describes the key approaches USAID will use to translate systems thinking into systems practice. It focuses on ways USAID can better understand and engage local systems to support them in producing more sustainable results. Systems thinking is a mindset and set of tools that we use to understand how systems behave and produce certain results or outcomes. Systems practice is the application of systems thinking to better understand challenges and strengthen the capacity of local systems to unlock locally led, sustained progress. The shift from systems thinking to systems practice is driven by a desire to integrate systems practice throughout the Program Cycle and increase our capacity to actively and adaptively manage programming in ways that recognize complexity and help make our programs more effective and sustainable.
These approaches will be utilized alongside and within the context of USAID’s policies and guidance, including technical guidance for specific sectors, as well as evidence and lessons learned from partners around the world. Systems thinking is a long-standing discipline that can serve as a powerful tool for understanding and working with local systems. It has been a consistent component of USAID’s decades-long commitment to locally led development and humanitarian assistance. USAID uses systems thinking to better understand the complex and interrelated challenges we confront – from climate change to migration to governance – and the perspectives of diverse stakeholders on these issues. When we understand challenges as complex systems – where outcomes emerge from the interactions and relationships between actors and elements in that system – we can leverage and help strengthen the local capacities and relationships that will ultimately drive sustainable progress…(More)”.
Social Systems Evidence
About: “…a continuously updated repository of syntheses of research evidence about the programs, services and products available in a broad range of government sectors and program areas (e.g., climate action, community and social services, economic development and growth, education, environmental conservation, education, housing and transportation) as well as the governance, financial and delivery arrangements within which these programs, services and products are provided, and the implementation strategies that can help to ensure that these programs, services and products get to those who need them.
The content covers the Sustainable Development Goals, with the exceptions of the health part of goal 3 (which is already well covered by existing databases).
The types of syntheses include evidence briefs for policy, overviews of evidence syntheses, evidence syntheses addressing questions about effectiveness, evidence syntheses addressing other types of questions, evidence syntheses in progress (i.e., protocols for evidence syntheses), and evidence syntheses being planned (i.e., registered titles for evidence syntheses). Social Systems Evidence also contains a continuously updated repository of economic evaluations in these same domains…(More)”
We are Developing AI at the Detriment of the Global South — How a Focus on Responsible Data Re-use Can Make a Difference
Article by Stefaan Verhulst and Peter Addo: “…At the root of this debate runs a frequent concern with how data is collected, stored, used — and responsibly reused for other purposes that initially collected for…
In this article, we propose that promoting responsible reuse of data requires addressing the power imbalances inherent in the data ecology. These imbalances disempower key stakeholders, thereby undermining trust in data management practices. As we recently argued in a report on “responsible data reuse in developing countries,” prepared for Agence Française de Development (AFD), power imbalences may be particularly pernicious when considering the use of data in the Global South. Addressing these requires broadening notions of consent, beyond current highly individualized approaches, in favor of what we instead term a social license for reuse.
In what follows, we explain what a social license means, and propose three steps to help achieve that goal. We conclude by calling for a new research agenda — one that would stretch existing disciplinary and conceptual boundaries — to reimagine what social licenses might mean, and how they could be operationalized…(More)”.
AI in Global Development Playbook
USAID Playbook: “…When used effectively and responsibly, AI holds the potential to accelerate progress on sustainable development and close digital divides, but it also poses risks that could further impede progress toward these goals. With the right enabling environment and ecosystem of actors, AI can enhance efficiency and accelerate development outcomes in sectors such as health, education, agriculture, energy, manufacturing, and delivering public services. The United States aims to ensure that the benefits of AI are shared equitably across the globe.
Distilled from consultations with hundreds of government officials, non-governmental organizations, technology firms and startups, and individuals from around the world, the AI in Global Development Playbook is a roadmap to develop the capacity, ecosystems, frameworks, partnerships, applications, and institutions to leverage safe, secure, and trustworthy AI for sustainable development.
The United States’ current efforts are grounded in the belief that AI, when developed and deployed responsibly, can be a powerful force for achieving the Sustainable Development Goals and addressing some of the world’s most urgent challenges. Looking ahead, the United States will continue to support low- and middle-income countries through funding, advocacy, and convening efforts–collectively navigating the complexities of the digital age and working toward a future in which the benefits of technological development are widely shared.
This Playbook seeks to underscore AI as a uniquely global opportunity with far-reaching impacts and potential risks. It highlights that safe, secure, and trustworthy design, deployment, and use of AI is not only possible but essential. Recognizing that international cooperation and multi-stakeholder partnerships are key in achieving progress, we invite others to contribute their expertise, resources, and perspectives to enrich and expand this framework.
The true measure of progress in responsible AI is not in the sophistication of our machines but in the quality of life the technology enhances. Together we can work toward ensuring the promise of AI is realized in service of this goal…(More)”
Making the Global Digital Compact a reality: Four steps to establish a responsible, inclusive and equitable data future.
Article by Stefaan Verhulst: “In September of this year, as world leaders assemble in New York for the 78th annual meeting of the United Nations (UN) General Assembly, they will confront a weighty agenda. War and peace will be at the forefront of conversations, along with efforts to tackle climate change and the ongoing migration crisis. Alongside these usual topics, however, the gathered dignitaries will also turn their attention to digital governance.
In 2021, the UN Secretary General proposed that a Global Digital Compact (GDC) be agreed upon that would “outline shared principles for an open, free and secure digital future for all”. The development of this Compact, which builds on a range of adjacent work streams at the UN, including activities related to the Sustainable Development Goals (SDGs), has now reached a vital inflection point. After a wide-ranging process of consultation, the General Assembly is expected to ratify the latest draft of the Digital Compact, which contains five key objectives and a commitment to thirteen cross-cutting principles. We have reached a rare moment of near-consensus in the global digital ecosystem, one that offers undeniable potential for revamping (and improving) our frameworks for global governance.
The Global Digital Compact will be agreed upon by UN Member States at the Summit of the Future at the United Nations Headquarters in New York, establishing guidelines for the responsible use and governance of digital technologies.
The growing prominence of these objectives and principles at the seat of global governance is a welcome development. Each is essential to developing a healthy, safe and responsible digital ecosystem. In particular, the emphasis on better data governance is a step forward, as is the related call for an enhanced approach for international AI governance. Both cannot be separated: data governance is the bedrock of AI governance.
Yet now that we are moving toward ratification of the Compact, we must focus on the next crucial—and in some ways most difficult – step: implementation. This is particularly important given that the digital realm faces in many ways a growing crisis of credibility, marked by growing concerns over exclusion, extraction, concentrations of power, mis- and disinformation, and what we have elsewhere referred to as an impending “data winter”.
Manifesting the goals of the Compact to create genuine and lasting impact is thus critical. In what follows, we explore four key ways in which the Compact’s key objectives can be operationalized to create a more vibrant, responsive and free global digital commons…(More)”.