AI could help scale humanitarian responses. But it could also have big downsides

Curated on Posted on by Stefaan Verhulst

Article by Thalia Beaty: “As the International Rescue Committee copes with dramatic increases in displaced people in recent years, the refugee aid organization has looked for efficiencies wherever it can — including using artificial intelligence.

Since 2015, the IRC has invested in Signpost — a portfolio of mobile apps and social media channels that answer questions in different languages for people in dangerous situations. The Signpost project, which includes many other organizations, has reached 18 million people so far, but IRC wants to significantly increase its reach by using AI tools — if they can do so safely.

Conflict, climate emergencies and economic hardship have driven up demand for humanitarian assistance, with more than 117 million people forcibly displaced in 2024, according to the United Nations refugee agency. The turn to artificial intelligence technologies is in part driven by the massive gap between needs and resources.

To meet its goal of reaching half of displaced people within three years, the IRC is testing a network of AI chatbots to see if they can increase the capacity of their humanitarian officers and the local organizations that directly serve people through Signpost. For now, the pilot project operates in El Salvador, Kenya, Greece and Italy and responds in 11 languages. It draws on a combination of large language models from some of the biggest technology companies, including OpenAI, Anthropic and Google.

The chatbot response system also uses customer service software from Zendesk and receives other support from Google and Cisco Systems.

If they decide the tools work, the IRC wants to extend the technical infrastructure to other nonprofit humanitarian organizations at no cost. They hope to create shared technology resources that less technically focused organizations could use without having to negotiate directly with tech companies or manage the risks of deployment…(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)”.

The Collaboration Playbook: A leader’s guide to cross-sector collaboration

Curated on Posted on by Stefaan Verhulst

Playbook by Ian Taylor and Nigel Ball: “The challenges facing our societies and economies today are so large and complex that, in many cases, cross-sector collaboration is not a choice, but an imperative. Yet collaboration remains elusive for many, often being put into the ‘too hard’ category. This playbook offers guidance on how we can seize collaboration opportunities successfully and rise to the challenges.

The recommendations in the playbook were informed by academic literature and practitioner experience. Rather than offer a procedural, step-by-step guide, this playbook offers provoking questions and frameworks that applies to different situations and objectives. While formal aspects such as contracts and procedures are well understood, it was found that what was needed was guidance on the intangible elements, sometimes referred to as ‘positive chemistry’. The significance of aspects like leadership, trust, culture, learning and power in cross-sector collaborations can be the game-changers for productive endeavours but are hard to get right.

Structured around these five key themes, the playbook presents 18 discreet ‘plays’ for effective collaboration. The plays allow the reader to delve into specific areas of interest to gain a deeper understanding of what it means for their collaborative work.

The intention of the playbook is to provide a resource that informs and guides cross-sector leaders. It will be especially relevant for those working in, and partnering with, central and local government in an effort to improve social outcomes…(More)”.

Predictability, AI, And Judicial Futurism: Why Robots Will Run The Law And Textualists Will Like It

Curated on Posted on by Stefaan Verhulst

Paper by Jack Kieffaber: “The question isn’t whether machines are going to replace judges and lawyers—they are. The question is whether that’s a good thing. If you’re a textualist, you have to answer yes. But you won’t—which means you’re not a textualist. Sorry.

Hypothetical: The year is 2030.  AI has far eclipsed the median federal jurist as a textual interpreter. A new country is founded; it’s a democratic republic that uses human legislators to write laws and programs a state-sponsored Large Language Model called “Judge.AI” to apply those laws to facts. The model makes judicial decisions as to conduct on the back end, but can also provide advisory opinions on the front end; if a citizen types in his desired action and hits “enter,” Judge.AI will tell him, ex ante, exactly what it would decide ex post if the citizen were to perform the action and be prosecuted. The primary result is perfect predictability; secondary results include the abolition of case law, the death of common law, and the replacement of all judges—indeed, all lawyers—by a single machine. Don’t fight the hypothetical, assume it works. This article poses the question:  Is that a utopia or a dystopia?

If you answer dystopia, you cannot be a textualist. Part I of this article establishes why:  Because predictability is textualism’s only lodestar, and Judge.AI is substantially more predictable than any regime operating today. Part II-A dispatches rebuttals premised on positive nuances of the American system; such rebuttals forget that my hypothetical presumes a new nation and take for granted how much of our nation’s founding was premised on mitigating exactly the kinds of human error that Judge.AI would eliminate. And Part II-B dispatches normative rebuttals, which ultimately amount to moral arguments about objective good—which are none of the textualist’s business. 

When the dust clears, you have only two choices: You’re a moralist, or you’re a formalist. If you’re the former, you’ll need a complete account of the objective good—which has evaded man for his entire existence. If you’re the latter, you should relish the fast-approaching day when all laws and all lawyers are usurped by a tin box.  But you’re going to say you’re something in between. And you’re not…(More)”

The Next Phase of the Data Economy: Economic & Technological Perspectives

Curated on Posted on by Stefaan Verhulst

Paper by Jad Esber et al: The data economy is poised to evolve toward a model centered on individual agency and control, moving us toward a world where data is more liquid across platforms and applications. In this future, products will either utilize existing personal data stores or create them when they don’t yet exist, empowering individuals to fully leverage their own data for various use cases.

The analysis begins by establishing a foundation for understanding data as an economic good and the dynamics of data markets. The article then investigates the concept of personal data stores, analyzing the historical challenges that have limited their widespread adoption. Building on this foundation, the article then considers how recent shifts in regulation, technology, consumer behavior, and market forces are converging to create new opportunities for a user-centric data economy. The article concludes by discussing potential frameworks for value creation and capture within this evolving paradigm, summarizing key insights and potential future directions for research, development, and policy.

We hope this article can help shape the thinking of scholars, policymakers, investors, and entrepreneurs, as new data ownership and privacy technologies emerge, and regulatory bodies around the world mandate open flows of data and new terms of service intended to empower users as well as small-to-medium–sized businesses…(More)”.

The Emergence of National Data Initiatives: Comparing proposals and initiatives in the United Kingdom, Germany, and the United States

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst and Roshni Singh: “Governments are increasingly recognizing data as a pivotal asset for driving economic growth, enhancing public service delivery, and fostering research and innovation. This recognition has intensified as policymakers acknowledge that data serves as the foundational element of artificial intelligence (AI) and that advancing AI sovereignty necessitates a robust data ecosystem. However, substantial portions of generated data remain inaccessible or underutilized. In response, several nations are initiating or exploring the launch of comprehensive national data strategies designed to consolidate, manage, and utilize data more effectively and at scale. As these initiatives evolve, discernible patterns in their objectives, governance structures, data-sharing mechanisms, and stakeholder engagement frameworks reveal both shared principles and country-specific approaches.

This blog seeks to start some initial research on the emergence of national data initiatives by examining three national data initiatives and exploring their strategic orientations and broader implications. They include:

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)”.

Data for Better Governance: Building Government Analytics Ecosystems in Latin America and the Caribbean

Curated on Posted on by Stefaan Verhulst

Report by the Worldbank: “Governments in Latin America and the Caribbean face significant development challenges, including insufficient economic growth, inflation, and institutional weaknesses. Overcoming these issues requires identifying systemic obstacles through data-driven diagnostics and equipping public officials with the skills to implement effective solutions.

Although public administrations in the region often have access to valuable data, they frequently fall short in analyzing it to inform decisions. However, the impact is big. Inefficiencies in procurement, misdirected transfers, and poorly managed human resources result in an estimated waste of 4% of GDP, equivalent to 17% of all public spending. 

The report “Data for Better Governance: Building Government Analytical Ecosystems in Latin America and the Caribbean” outlines a roadmap for developing government analytics, focusing on key enablers such as data infrastructure and analytical capacity, and offers actionable strategies for improvement…(More)”.

An Open Source Python Library for Anonymizing Sensitive Data

Curated on Posted on by Stefaan Verhulst

Paper by Judith Sáinz-Pardo Díaz & Álvaro López García: “Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to meet in compliance with strict data protection regulations. Consequently, researchers need to rely on proven methods that allow them to anonymize their data without sharing it with third parties. To this end, this paper presents the implementation of a Python library for the anonymization of sensitive tabular data. This framework provides users with a wide range of anonymization methods that can be applied on the given dataset, including the set of identifiers, quasi-identifiers, generalization hierarchies and allowed level of suppression, along with the sensitive attribute and the level of anonymity required. The library has been implemented following best practices for integration and continuous development, as well as the use of workflows to test code coverage based on unit and functional tests…(More)”.

Informality in Policymaking

Curated on Posted on by Stefaan Verhulst

Book edited by Lindsey Garner-Knapp, Joanna Mason, Tamara Mulherin and E. Lianne Visser: “Public policy actors spend considerable time writing policy, advising politicians, eliciting stakeholder views on policy concerns, and implementing initiatives. Yet, they also ‘hang out’ chatting at coffee machines, discuss developments in the hallway walking from one meeting to another, or wander outside to carparks for a quick word and to avoid prying eyes. Rather than interrogating the rules and procedures which govern how policies are made, this volume asks readers to begin with the informal as a concept and extend this to what people do, how they relate to each other, and how this matters.

Emerging from a desire to enquire into the lived experience of policy professionals, and to conceptualise afresh the informal in the making of public policy, Informality in Policymaking explores how informality manifests in different contexts, spaces, places, and policy arenas, and the implications of this. Including nine empirical chapters, this volume presents studies from around the world and across policy domains spanning the rural and urban, and the local to the supranational. The chapters employ interdisciplinary approaches and integrate creative elements, such as drawings of hand gestures and fieldwork photographs, in conjunction with ethnographic ‘thick descriptions’.

In unveiling the realities of how policy is made, this deeply meaningful and thoughtfully constructed collection argues that the formal is only part of the story of policymaking, and thus only part of the solutions it seeks to create. Informality in Policymaking will be of interest to researchers and policymakers alike…(More)”.