A matter of choice: People and possibilities in the age of AI

Curated on Posted on by Stefaan Verhulst

UNDP Human Development Report 2025: “Artificial intelligence (AI) has broken into a dizzying gallop. While AI feats grab headlines, they privilege technology in a make-believe vacuum, obscuring what really matters: people’s choices.

The choices that people have and can realize, within ever expanding freedoms, are essential to human development, whose goal is for people to live lives they value and have reason to value. A world with AI is flush with choices the exercise of which is both a matter of human development and a means to advance it.

Going forward, development depends less on what AI can do—not on how human-like it is perceived to be—and more on mobilizing people’s imaginations to reshape economies and societies to make the most of it. Instead of trying vainly to predict what will happen, this year’s Human Development Report asks what choices can be made so that new development pathways for all countries dot the horizon, helping everyone have a shot at thriving in a world with AI…(More)”.

Charting the AI for Good Landscape – A New Look

Curated on Posted on by Stefaan Verhulst

Article by Perry Hewitt and Jake Porway: “More than 50% of nonprofits report that their organization uses generative AI in day-to-day operations. We’ve also seen an explosion of AI tools and investments. 10% of all the AI companies that exist in the US were founded in 2022, and that number has likely grown in subsequent years.  With investors funneling over $300B into AI and machine learning startups, it’s unlikely this trend will reverse any time soon.

Not surprisingly, the conversation about Artificial Intelligence (AI) is now everywhere, spanning from commercial uses such as virtual assistants and consumer AI to public goods, like AI-driven drug discovery and chatbots for education. The dizzying amount of new AI programs and initiatives – over 5000 new tools listed in 2023 on AI directories like TheresAnAI alone – can make the AI landscape challenging to navigate in general, much less for social impact. Luckily, four years ago, we surveyed the Data and AI for Good landscape and mapped out distinct families of initiatives based on their core goals. Today, we are revisiting that landscape to help folks get a handle on the AI for Good landscape today and to reflect on how the field has expanded, diversified, and matured…(More)”.

Macron announces citizens’ convention on school schedules, a move met with skepticism

Curated on Posted on by Stefaan Verhulst

Article by Violaine Morin: “The French president wants to restructure how schoolchildren’s time is managed, tackling a highly sensitive issue. Teachers’ unions called it a diversion. The plan had been rumored quietly for some time, but it is now official: President Emmanuel Macron announced on Friday, May 2, speaking to Le Parisien, the launch of a citizens’ convention on “the time of children” and “the organization of the day for children aged 3 to 18.”

“It seems necessary to me to ensure that the organization of our pupils’ days is more conducive to their development and learning, and that a balance is found to also facilitate family life,” said Macron, adding that he hoped for “numerous agreements among all those affected […], parents, the educational and extracurricular community, local authorities and even tourism professionals.”

The format of citizens’ conventions involves a randomly selected panel of French citizens meeting with stakeholders, as was the case for the two previous conventions on climate and end-of-life care. The convention could start in early June and last into the fall, as Le Parisien first reported, information also confirmed to Le Monde by the Elysée Palace. Its organization will be entrusted to the Economic, Social and Environmental Council (CESE), an advisory body in which unions, employers and nonprofits are represented.The prime minister’s office confirmed to Le Monde that a letter with instructions had been sent to the CESE…(More)”

The RRI Citizen Review Panel: a public engagement method for supporting responsible territorial policymaking

Curated on Posted on by Stefaan Verhulst

Paper by Maya Vestergaard Bidstrup et al: “Responsible Territorial Policymaking incorporates the main principles of Responsible Research and Innovation (RRI) into the policymaking process, making it well-suited for guiding the development of sustainable and resilient territorial policies that prioritise societal needs. As a cornerstone in RRI, public engagement plays a central role in this process, underscoring the importance of involving all societal actors to align outcomes with the needs, expectations, and values of society. In the absence of existing methods to gather sufficiently and effectively the citizens’ review of multiple policies at a territorial level, the RRI Citizen Review Panel is a new public engagement method developed to facilitate citizens’ review and validation of territorial policies. By using RRI as an analytical framework, this paper examines whether the RRI Citizen Review Panel can support Responsible Territorial Policymaking, not only by incorporating citizens’ perspectives into territorial policymaking, but also by making policies more responsible. The paper demonstrates that in the review of territorial policies, citizens are adding elements of RRI to a wide range of policies within different policy areas, contributing to making policies more responsible. Consequently, the RRI Citizen Review Panel emerges as a valuable tool for policymakers, enabling them to gather citizen perspectives and imbue policies with a heightened sense of responsibility…(More)”.

Playing for science: Designing science games

Curated on Posted on by Stefaan Verhulst

Paper by Claudio M Radaelli: “How can science have more impact on policy decisions? The P-Cube Project has approached this question by creating five pedagogical computer games based on missions given to a policy entrepreneur (the player) advocating for science-informed policy decisions. The player explores simplified strategies for policy change rooted in a small number of variables, thus making it possible to learn without a prior background in political science or public administration. The games evolved from the intuition that, instead of making additional efforts to explain science to decision-makers, we should directly empower would-be scientists (our primary audience for the games), post-graduates in public policy and administration, and activists for science. The two design principles of the games revolve around learning about how policy decisions are made (a learning-about-content principle) and reflection. Indeed, the presence of science in the policy process raises ethical and normative decisions, especially when we consider controversial strategies like civil disobedience and alliances with industry. To be on the side of science does not mean to be outside society and politics. I show the motivation, principles, scripts and pilots of the science games, reflecting on how they can be used and for what reasons…(More)”

Smart Cities:Technologies and Policy Options to Enhance Services and Transparency

Curated on Posted on by Stefaan Verhulst

GAO Report: “Cities across the nation are using “smart city” technologies like traffic cameras and gunshot detectors to improve public services. In this technology assessment, we looked at their use in transportation and law enforcement.

Experts and city officials reported multiple benefits. For example, Houston uses cameras and Bluetooth sensors to measure traffic flow and adjust signal timing. Other cities use license plate readers to find stolen vehicles.

But the technologies can be costly and the benefits unclear. The data they collect may be sold, raising privacy and civil liberties concerns. We offer three policy options to address such challenges…(More)”.

Data Commons: The Missing Infrastructure for Public Interest Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst, Burton Davis and Andrew Schroeder: “Artificial intelligence is celebrated as the defining technology of our time. From ChatGPT to Copilot and beyond, generative AI systems are reshaping how we work, learn, and govern. But behind the headline-grabbing breakthroughs lies a fundamental problem: The data these systems depend on to produce useful results that serve the public interest is increasingly out of reach.

Without access to diverse, high-quality datasets, AI models risk reinforcing bias, deepening inequality, and returning less accurate, more imprecise results. Yet, access to data remains fragmented, siloed, and increasingly enclosed. What was once open—government records, scientific research, public media—is now locked away by proprietary terms, outdated policies, or simple neglect. We are entering a data winter just as AI’s influence over public life is heating up.

This isn’t just a technical glitch. It’s a structural failure. What we urgently need is new infrastructure: data commons.

A data commons is a shared pool of data resources—responsibly governed, managed using participatory approaches, and made available for reuse in the public interest. Done correctly, commons can ensure that communities and other networks have a say in how their data is used, that public interest organizations can access the data they need, and that the benefits of AI can be applied to meet societal challenges.

Commons offer a practical response to the paradox of data scarcity amid abundance. By pooling datasets across organizations—governments, universities, libraries, and more—they match data supply with real-world demand, making it easier to build AI that responds to public needs.

We’re already seeing early signs of what this future might look like. Projects like Common Corpus, MLCommons, and Harvard’s Institutional Data Initiative show how diverse institutions can collaborate to make data both accessible and accountable. These initiatives emphasize open standards, participatory governance, and responsible reuse. They challenge the idea that data must be either locked up or left unprotected, offering a third way rooted in shared value and public purpose.

But the pace of progress isn’t matching the urgency of the moment. While policymakers debate AI regulation, they often ignore the infrastructure that makes public interest applications possible in the first place. Without better access to high-quality, responsibly governed data, AI for the common good will remain more aspiration than reality.

That’s why we’re launching The New Commons Challenge—a call to action for universities, libraries, civil society, and technologists to build data ecosystems that fuel public-interest AI…(More)”.

Entering the Vortex

Curated on Posted on by Stefaan Verhulst

Essay by Nils Gilman: “A strange and unsettling weather pattern is forming over the landscape of scholarly research. For decades, the climate of academic inquiry was shaped by a prevailing high-pressure system, a consensus grounded in the vision articulated by Vannevar Bush in “Science: The Endless Frontier” (1945). That era was characterized by robust federal investment, a faith in the university as the engine of basic research, and a compact that traded public funding for scientific autonomy and the promise of long-term societal benefit. It was a climate conducive to the slow, deliberate, and often unpredictable growth of knowledge, nurtured by a diverse ecosystem of human researchers — the vital “seed stock” of intellectual discovery.

But that high-pressure system is collapsing. A brutal, unyielding cold front of academic defunding has swept across the nation, a consequence of shifting political priorities, populist resentment, and a calculated assault on the university as an institution perceived as hostile to certain political agendas. This is not merely a belt-tightening exercise; it is, for all intents and purposes, the dismantling of Vannevar Bush’s Compact, the end of the era of “big government”-funded Wissenschaft. Funding streams for basic research are dwindling, grant applications face increasingly long odds, and the financial precarity of academic careers deters the brightest minds. The human capital necessary for sustained, fundamental inquiry is beginning to wither.

Simultaneously, a warm, moisture-laden airmass is rapidly advancing: the astonishing rise of AI-based research tools. Powered by vast datasets and sophisticated algorithms, these tools promise to revolutionize every stage of the research process – from literature review and data analysis to hypothesis generation and the drafting of scholarly texts. As a recent New Yorker piece on AI and the humanities suggests, these AI engines can already generate deep research and coherent texts on virtually any subject, seemingly within moments. They offer the prospect of unprecedented efficiency, speed, and scale in the production of scholarly output.

The collision of these two epochal weather systems — the brutal cold front of academic defunding and the warm, expansive airmass of AI-based research tools — is creating an atmospheric instability unlike anything the world of scholarship has ever witnessed. Along the front where these forces meet, a series of powerful and unpredictable tornados are beginning to touch down, reshaping the terrain of knowledge production in real-time…(More)”.

Our new AI strategy puts Wikipedia’s humans first

Curated on Posted on by Stefaan Verhulst

Blog by Chris Albon and Leila Zia: “Not too long ago, we were asked when we’re going to replace Wikipedia’s human-curated knowledge with AI. 

The answer? We’re not.

The community of volunteers behind Wikipedia is the most important and unique element of Wikipedia’s success. For nearly 25 years, Wikipedia editors have researched, deliberated, discussed, built consensus, and collaboratively written the largest encyclopedia humankind has ever seen. Their care and commitment to reliable encyclopedic knowledge is something AI cannot replace. 

That is why our new AI strategy doubles down on the volunteers behind Wikipedia.

We will use AI to build features that remove technical barriers to allow the humans at the core of Wikipedia to spend their valuable time on what they want to accomplish, and not on how to technically achieve it. Our investments will be focused on specific areas where generative AI excels, all in the service of creating unique opportunities that will boost Wikipedia’s volunteers: 

  • Supporting Wikipedia’s moderators and patrollers with AI-assisted workflows that automate tedious tasks in support of knowledge integrity; 
  • Giving Wikipedia’s editors time back by improving the discoverability of information on Wikipedia to leave more time for human deliberation, judgment, and consensus building; 
  • Helping editors share local perspectives or context by automating the translation and adaptation of common topics;
  • Scaling the onboarding of new Wikipedia volunteers with guided mentorship. 

You can read the Wikimedia Foundation’s new AI strategy over on Meta-Wiki…(More)”.

Real-time prices, real results: comparing crowdsourcing, AI, and traditional data collection

Curated on Posted on by Stefaan Verhulst

Article by Julius Adewopo, Bo Andree, Zacharey Carmichael, Steve Penson, Kamwoo Lee: “Timely, high-quality food price data is essential for shock responsive decision-making. However, in many low- and middle-income countries, such data is often delayed, limited in geographic coverage, or unavailable due to operational constraints. Traditional price monitoring, which relies on structured surveys conducted by trained enumerators, is often constrained by challenges related to cost, frequency, and reach.

To help overcome these limitations, the World Bank launched the Real-Time Prices (RTP) data platform. This effort provides monthly price data using a machine learning framework. The models combine survey results with predictions derived from observations in nearby markets and related commodities. This approach helps fill gaps in local price data across a basket of goods, enabling real-time monitoring of inflation dynamics even when survey data is incomplete or irregular.

In parallel, new approaches—such as citizen-submitted (crowdsourced) data—are being explored to complement conventional data collection methods. These crowdsourced data were recently published in a Nature Scientific Data paper. While the adoption of these innovations is accelerating, maintaining trust requires rigorous validation.

A newly published study in PLOS compares the two emerging methods with the traditional, enumerator-led gold standard, providing  new evidence that both crowdsourced and AI-imputed prices can serve as credible, timely alternatives to traditional ground-truth data collection—especially in contexts where conventional methods face limitations…(More)”.