The Case for Local and Regional Public Engagement in Governing Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst and Claudia Chwalisz: “As the Paris AI Action Summit approaches, the world’s attention will once again turn to the urgent questions surrounding how we govern artificial intelligence responsibly. Discussions will inevitably include calls for global coordination and participation, exemplified by several proposals for a Global Citizens’ Assembly on AI. While such initiatives aim to foster inclusivity, the reality is that meaningful deliberation and actionable outcomes often emerge most effectively at the local and regional levels.

Building on earlier reflections in “AI Globalism and AI Localism,” we argue that to govern AI for public benefit, we must prioritize building public engagement capacity closer to the communities where AI systems are deployed. Localized engagement not only ensures relevance to specific cultural, social, and economic contexts but also equips communities with the agency to shape both policy and product development in ways that reflect their needs and values.

While a Global Citizens’ Assembly sounds like a great idea on the surface, there is no public authority with teeth or enforcement mechanisms at that level of governance. The Paris Summit represents an opportunity to rethink existing AI governance frameworks, reorienting them toward an approach that is grounded in lived, local realities and mutually respectful processes of co-creation. Toward that end, we elaborate below on proposals for: local and regional AI assemblies; AI citizens’ assemblies for EU policy; capacity-building programs, and localized data governance models…(More)”.

Good government data requires good statistics officials – but how motivated and competent are they?

Curated on Posted on by Stefaan Verhulst

Worldbank Blog: “Government data is only as reliable as the statistics officials who produce it. Yet, surprisingly little is known about these officials themselves. For decades, they have diligently collected data on others –  such as households and firms – to generate official statistics, from poverty rates to inflation figures. Yet, data about statistics officials themselves is missing. How competent are they at analyzing statistical data? How motivated are they to excel in their roles? Do they uphold integrity when producing official statistics, even in the face of opposing career incentives or political pressures? And what can National Statistical Offices (NSOs) do to cultivate a workforce that is competent, motivated, and ethical?

We surveyed 13,300 statistics officials in 14 countries in Latin America and the Caribbean to find out. Five results stand out. For further insights, consult our Inter-American Development Bank (IDB) report, Making National Statistical Offices Work Better.

1. The competence and management of statistics officials shape the quality of statistical data

Our survey included a short exam assessing basic statistical competencies, such as descriptive statistics and probability. Statistical competence correlates with data quality: NSOs with higher exam scores among employees tend to achieve better results in the World Bank’s Statistical Performance Indicators (r = 0.36).

NSOs with better management practices also have better statistical performance. For instance, NSOs with more robust recruitment and selection processes have better statistical performance (r = 0.62)…(More)”.

Which Health Facilities Have Been Impacted by L.A.-Area Fires? AI May Paint a Clearer Picture

Curated on Posted on by Stefaan Verhulst

Article by Andrew Schroeder: “One of the most important factors for humanitarian responders in these types of large-scale disaster situations is to understand the effects on the formal health system, upon which most people — and vulnerable communities in particular — rely upon in their neighborhoods. Evaluation of the impact of disasters on individual structures, including critical infrastructure such as health facilities, is traditionally a relatively slow and manually arduous process, involving extensive ground truth visitation by teams of assessment professionals.

Speeding up this process without losing accuracy, while potentially improving the safety and efficiency of assessment teams, is among the more important analytical efforts Direct Relief can undertake for response and recovery efforts. Manual assessments can now be effectively paired with AI-based analysis of satellite imagery to do just that…

With the advent of geospatial AI models trained on disaster damage impacts, ground assessment is not the only tool available to response agencies and others seeking to understand how much damage has occurred and the degree to which that damage may affect essential services for communities. The work of the Oregon State University team of experts in remote sensing-based post-disaster damage detection, led by Jamon Van Den Hoek and Corey Scher, was featured in the Financial Times on January 9.

Their modeling, based on Sentinel-1 satellite imagery, identified 21,757 structures overall, of which 11,124 were determined to have some level of damage. The Oregon State model does not distinguish between different levels of damage, and therefore cannot respond to certain types of questions that the manual inspections can respond to, but nevertheless the coverage area and the speed of detection have been much greater…(More)”.

Kickstarting Collaborative, AI-Ready Datasets in the Life Sciences with Government-funded Projects

Curated on Posted on by Stefaan Verhulst

Article by Erika DeBenedictis, Ben Andrew & Pete Kelly: “In the age of Artificial Intelligence (AI), large high-quality datasets are needed to move the field of life science forward. However, the research community lacks strategies to incentivize collaboration on high-quality data acquisition and sharing. The government should fund collaborative roadmapping, certification, collection, and sharing of large, high-quality datasets in life science. In such a system, nonprofit research organizations engage scientific communities to identify key types of data that would be valuable for building predictive models, and define quality control (QC) and open science standards for collection of that data. Projects are designed to develop automated methods for data collection, certify data providers, and facilitate data collection in consultation with researchers throughout various scientific communities. Hosting of the resulting open data is subsidized as well as protected by security measures. This system would provide crucial incentives for the life science community to identify and amass large, high-quality open datasets that will immensely benefit researchers…(More)”.

Announcing SPARROW: A Breakthrough AI Tool to Measure and Protect Earth’s Biodiversity in the Most Remote Places

Curated on Posted on by Stefaan Verhulst

Blog by Juan Lavista Ferres: “The biodiversity of our planet is rapidly declining. We’ve likely reached a tipping point where it is crucial to use every tool at our disposal to help preserve what remains. That’s why I am pleased to announce SPARROW—Solar-Powered Acoustic and Remote Recording Observation Watch, developed by Microsoft’s AI for Good Lab. SPARROW is an AI-powered edge computing solution designed to operate autonomously in the most remote corners of the planet. Solar-powered and equipped with advanced sensors, it collects biodiversity data—from camera traps, acoustic monitors, and other environmental detectors—that are processed using our most advanced PyTorch-based wildlife AI models on low-energy edge GPUs. The resulting critical information is then transmitted via low-Earth orbit satellites directly to the cloud, allowing researchers to access fresh, actionable insights in real time, no matter where they are. 

Think of SPARROW as a network of Earth-bound satellites, quietly observing and reporting on the health of our ecosystems without disrupting them. By leveraging solar energy, these devices can run for a long time, minimizing their footprint and any potential harm to the environment…(More)”.

It Was the Best of Times, It Was the Worst of Times: The Dual Realities of Data Access in the Age of Generative AI

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst: “It was the best of times, it was the worst of times… It was the spring of hope, it was the winter of despair.” –Charles Dickens, A Tale of Two Cities

Charles Dickens’s famous line captures the contradictions of the present moment in the world of data. On the one hand, data has become central to addressing humanity’s most pressing challenges — climate change, healthcare, economic development, public policy, and scientific discovery. On the other hand, despite the unprecedented quantity of data being generated, significant obstacles remain to accessing and reusing it. As our digital ecosystems evolve, including the rapid advances in artificial intelligence, we find ourselves both on the verge of a golden era of open data and at risk of slipping deeper into a restrictive “data winter.”

A Tale of Two Cities by Charles Dickens (1902)

These two realities are concurrent: the challenges posed by growing restrictions on data reuse, and the countervailing potential brought by advancements in privacy-enhancing technologies (PETs), synthetic data, and data commons approaches. It argues that while current trends toward closed data ecosystems threaten innovation, new technologies and frameworks could lead to a “Fourth Wave of Open Data,” potentially ushering in a new era of data accessibility and collaboration…(More)” (First Published in Industry Data for Society Partnership’s (IDSP) 2024 Year in Review).

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)”.

The Emergent Landscape of Data Commons: A Brief Survey and Comparison of Existing Initiatives

Curated on Posted on by Stefaan Verhulst

Article by Stefaan G. Verhulst and Hannah Chafetz: With the increased attention on the need for data to advance AI, data commons initiatives around the world are redefining how data can be accessed, and re-used for societal benefit. These initiatives focus on generating access to data from various sources for a public purpose and are governed by communities themselves. While diverse in focus–from health and mobility to language and environmental data–data commons are united by a common goal: democratizing access to data to fuel innovation and tackle global challenges.

This includes innovation in the context of artificial intelligence (AI). Data commons are providing the framework to make pools of diverse data available in machine understandable formats for responsible AI development and deployment. By providing access to high quality data sources with open licensing, data commons can help increase the quantity of training data in a less exploitative fashion, minimize AI providers’ reliance on data extracted across the internet without an open license, and increase the quality of the AI output (while reducing mis-information).

Over the last few months, the Open Data Policy Lab (a collaboration between The GovLab and Microsoft) has conducted various research initiatives to explore these topics further and understand:

(1) how the concept of a data commons is changing in the context of artificial intelligence, and

(2) current efforts to advance the next generation of data commons.

In what follows we provide a summary of our findings thus far. We hope it inspires more data commons use cases for responsible AI innovation in the public’s interest…(More)”.

Two Open Science Foundations: Data Commons and Stewardship as Pillars for Advancing the FAIR Principles and Tackling Planetary Challenges

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst and Jean Claude Burgelman: “Today the world is facing three major planetary challenges: war and peace, steering Artificial Intelligence and making the planet a healthy Anthropoceen. As they are closely interrelated, they represent an era of “polycrisis”, to use the term Adam Tooze has coined. There are no simple solutions or quick fixes to these (and other) challenges; their interdependencies demand a multi-stakeholder, interdisciplinary approach.

As world leaders and experts convene in Baku for The 29th session of the Conference of the Parties to the United Nations Framework Convention on Climate Change (COP29), the urgency of addressing these global crises has never been clearer. A crucial part of addressing these challenges lies in advancing science — particularly open science, underpinned by data made available leveraging the FAIR principles (Findable, Accessible, Interoperable, and Reusable). In this era of computation, the transformative potential of research depends on the seamless flow and reuse of high-quality data to unlock breakthrough insights and solutions. Ensuring data is available in reusable, interoperable formats not only accelerates the pace of scientific discovery but also expedites the search for solutions to global crises.

Image of the retreat of the Columbia glacier by Jesse Allen, using Landsat data from the U.S. Geological Survey. Free to re-use from NASA Visible Earth.

While FAIR principles provide a vital foundation for making data accessible, interoperable and reusable, translating these principles into practice requires robust institutional approaches. Toward that end, in the below, we argue two foundational pillars must be strengthened:

  • Establishing Data Commons: The need for shared data ecosystems where resources can be pooled, accessed, and re-used collectively, breaking down silos and fostering cross-disciplinary collaboration.
  • Enabling Data Stewardship: Systematic and responsible data reuse requires more than access; it demands stewardship — equipping institutions and scientists with the capabilities to maximize the value of data while safeguarding its responsible use is essential…(More)”.