China is building an entire empire on data

Curated on Posted on by Stefaan Verhulst

The Economist: “CHINA’S 1.1BN internet users churn out more data than anyone else on Earth. So does the country’s vast network of facial-recognition cameras. As autonomous cars speed down roads and flying ones criss-cross the skies, the quality and value of the information flowing from emerging technologies will soar. Yet the volume of data is not the only thing setting China apart. The government is also embedding data management into the economy and national security. That has implications for China, and holds lessons for democracies.

China’s planners see data as a factor of production, alongside labour, capital and land. Xi Jinping, the president, has called data a foundational resource “with a revolutionary impact” on international competition. The scope of this vision is unparalleled, affecting everything from civil liberties to the profits of internet firms and China’s pursuit of the lead in artificial intelligence.

Mr Xi’s vision is being enacted fast. In 2021 China released rules modelled on Europe’s General Data Protection Regulation (GDPR). Now it is diverging quickly from Western norms. All levels of government are to marshal the data resources they have. A sweeping project to assess the data piles at state-owned firms is under way. The idea is to value them as assets, and add them to balance-sheets or trade them on state-run exchanges. On June 3rd the State Council released new rules to compel all levels of government to share data.

Another big step is a digital ID, due to be launched on July 15th. Under this, the central authorities could control a ledger of every person’s websites and apps. Connecting someone’s name with their online activity will become harder for the big tech firms which used to run the system. They will see only an anonymised stream of digits and letters. Chillingly, however, the ledger may one day act as a panopticon for the state.

China’s ultimate goal appears to be to create an integrated national data ocean, covering not just consumers but industrial and state activity, too. The advantages are obvious, and include economies of scale for training AI models and lower barriers to entry for small new firms…(More)”.

Bad data leads to bad policy

Curated on Posted on by Stefaan Verhulst

Article by Georges-Simon Ulrich: “When the UN created a Statistical Commission in 1946, the world was still recovering from the devastation of the second world war. Then, there was broad consensus that only reliable, internationally comparable data could prevent conflict, combat poverty and anchor global co-operation. Nearly 80 years later, this insight remains just as relevant, but the context has changed dramatically…

This erosion of institutional capacity could not come at a more critical moment. The UN is unable to respond adequately as it is facing a staffing shortfall itself. Due to ongoing austerity measures at the UN, many senior positions remain vacant, and the director of the UN Statistics Division has retired, with no successor appointed. This comes at a time when bold and innovative initiatives — such as a newly envisioned Trusted Data Observatory — are urgently needed to make official statistics more accessible and machine-readable.

Meanwhile, the threat of targeted disinformation is growing. On social media, distorted or manipulated content spreads at unprecedented speed. Emerging tools like AI chatbots exacerbate the problem. These systems rely on web content, not verified data, and are not built to separate truth from falsehood. Making matters worse, many governments cannot currently make their data usable for AI because it is not standardised, not machine-readable, or not openly accessible. The space for sober, evidence-based discourse is shrinking.

This trend undermines public trust in institutions, strips policymaking of its legitimacy, and jeopardises the UN Sustainable Development Goals (SDGs). Without reliable data, governments will be flying blind — or worse: they will be deliberately misled.

When countries lose control of their own data, or cannot integrate it into global decision-making processes, they become bystanders to their own development. Decisions about their economies, societies and environments are then outsourced to AI systems trained on skewed, unrepresentative data. The global south is particularly at risk, with many countries lacking access to quality data infrastructures. In countries such as Ethiopia, unverified information spreading rapidly on social media has fuelled misinformation-driven violence.

The Covid-19 pandemic demonstrated that strong data systems enable better crisis response. To counter these risks, the creation of a global Trusted Data Observatory (TDO) is essential. This UN co-ordinated, democratically governed platform would help catalogue and make accessible trusted data around the world — while fully respecting national sovereignty…(More)”

Community Engagement Is Crucial for Successful State Data Efforts

Curated on Posted on by Stefaan Verhulst

Resource by the Data Quality Campaign: “Engaging communities is a critical step toward ensuring that data efforts work for their intended audiences. People, including state policymakers, school leaders, families, college administrators, employers, and the public, should have a say in how their state provides access to education and workforce data. And as state leaders build robust statewide longitudinal data systems (SLDSs) or move other data efforts forward, they must deliberately create consistent opportunities for communities to weigh in. This resource explores how states can meaningfully engage with communities to build trust and improve data efforts by ensuring that systems, tools, and resources are valuable to the people who use them…(More)”.

Data Collection and Analysis for Policy Evaluation: Not for Duty, but for Knowledge

Curated on Posted on by Stefaan Verhulst

Paper by Valentina Battiloro: “This paper explores the challenges and methods involved in public policy evaluation, focusing on the role of data collection and use. The term “evaluation” encompasses a variety of analyses and approaches, all united by the intent to provide a judgment on a specific policy, but which, depending on the precise knowledge objective, can translate into entirely different activities. Regardless of the type of evaluation, a brief overview of which is provided, the collection of information represents a priority, often undervalued, under the assumption that it is sufficient to “have the data.“ Issues arise concerning the precise definition of the design, the planning of necessary information collection, and the appropriate management of timelines. With regard to administrative data, a potentially valuable source, a number of unresolved challenges remain due to a weak culture of data utilization. Among these are the transition from an administrative data culture to a statistical data culture, and the fundamental issue of microdata accessibility for research purposes, which is currently hindered by significant barriers…(More)”.

Tech: When Silicon Valley Remakes the World

Curated on Posted on by Stefaan Verhulst

Book by Olivier Alexandre: “Sometimes only an outsider can show how an industry works—and how that industry works upon the world. In Tech, sociologist Olivier Alexandre takes us on a revealing tour of Silicon Valley’s prominent personalities and vibrant networks to capture the way its denizens live, think, relate, and innovate, and how they shape the very code and conduct of business itself.
 
Even seasoned observers will gain insight into the industry’s singular milieu from Alexandre’s piercing eye. He spends as much time with Silicon Valley’s major players as with those who fight daily to survive within a system engineered for disruption. Embedded deep within the community, Alexandre accesses rooms shut tight to the public and reports back on the motivations, ambitions, and radical vision guiding tech companies. From the conquest of space to quantum computing, engineers have recast the infinitely large and small. Some scientists predict the end of death and the replacement of human beings with machines. But at what cost? Alexandre sees a shadow hanging over the Valley, jeopardizing its future and the economy made in its image. Critical yet fair, Tech illuminates anew a world of perpetual revolution…(More)”.

Companies Are Missing The Chance To Improve The World With Their Data

Curated on Posted on by Stefaan Verhulst

Article by Nino Letteriello: “This September will mark two years since the Data Governance Act officially became applicable across the European Union. This regulation, part of the broader European data strategy, focuses primarily on data sharing between public and private entities and the overall development of a data-driven economy.

Although less known than its high-profile counterparts—the Data Act and especially the Artificial Intelligence Act—the Data Governance Act introduces a particularly compelling concept: data altruism.

Data altruism refers to the voluntary sharing of data—by individuals or companies—without expecting any reward for purposes of general interest. Such data has immense potential to advance research and drive innovation in areas like healthcare, environmental sustainability and mobility…The absence of structured research into corporate resistance to data donation suggests that the topic remains niche—mostly embraced by tech giants with strong data capabilities and CSR programs, like Meta for Good and Google AI for Good—but still virtually unknown to most companies.

Before we talk about resistance to data donation, perhaps we should explore the level of awareness companies have about the impact such donations could have.

And so, in trying to answer the question I posed at the beginning of this article, perhaps the most appropriate response is yet another question: Do companies even realize that the data they collect, generate and manage could be a vital resource for building a better world?

And if they were more aware of the different ways they could do good with data—would they be more inclined to act?

Despite the existence of the Data Governance Act and the Data Act, these questions remain largely unanswered. But the hope is that, as data becomes more democratized within organizations and as social responsibility and sustainability take center stage, “Data for Good” will become a standard theme in corporate agendas.

After all, private companies are the most valuable and essential data providers and partners for this kind of transformation—and it is often we, the people, who provide them with the very data that could help change our world…(More)”.

What Counts as Discovery?

Curated on Posted on by Stefaan Verhulst

Essay by Nisheeth Vishnoi: “Long before there were “scientists,” there was science. Across every continent, humans developed knowledge systems grounded in experience, abstraction, and prediction—driven not merely by curiosity, but by a desire to transform patterns into principles, and observation into discovery. Farmers tracked solstices, sailors read stars, artisans perfected metallurgy, and physicians documented plant remedies. They built calendars, mapped cycles, and tested interventions—turning empirical insight into reliable knowledge.

From the oral sciences of Africa, which encoded botanical, medical, and ecological knowledge across generations, to the astronomical observatories of Mesoamerica, where priests tracked solstices, eclipses, and planetary motion with remarkable accuracy, early human civilizations sought more than survival. In Babylon, scribes logged celestial movements and built predictive models; in India, the architects of Vedic altars designed ritual structures whose proportions mirrored cosmic rhythms, embedding arithmetic and geometry into sacred form. Across these diverse cultures, discovery was not a separate enterprise—it was entwined with ritual, survival, and meaning. Yet the tools were recognizably scientific: systematic observation, abstraction, and the search for hidden order.

This was science before the name. And it reminds us that discovery has never belonged to any one civilization or era. Discovery is not intelligence itself, but one of its sharpest expressions—an act that turns perception into principle through a conceptual leap. While intelligence is broader and encompasses adaptation, inference, and learning in various forms (biological, cultural, and even mechanical), discovery marks those moments when something new is framed, not just found. 

Life forms learn, adapt, and even innovate. But it is humans who turned observation into explanation, explanation into abstraction, and abstraction into method. The rise of formal science brought mathematical structure and experiment, but it did not invent the impulse to understand—it gave it form, language, and reach.

And today, we stand at the edge of something unfamiliar: the possibility of lifeless discoveries. Artificial Intelligence machines, built without awareness or curiosity, are beginning to surface patterns and propose explanations, sometimes without our full understanding. If science has long been a dialogue between the world and living minds, we are now entering a strange new phase: abstraction without awareness, discovery without a discoverer.

AI systems now assist in everything from understanding black holes to predicting protein folds and even symbolic equation discovery. They parse vast datasets, detect regularities, and generate increasingly sophisticated outputs. Some claim they’re not just accelerating research, but beginning to reshape science itself—perhaps even to discover.

But what truly counts as a scientific discovery? This essay examines that question…(More)”

AI Scraping Bots Are Breaking Open Libraries, Archives, and Museums

Curated on Posted on by Stefaan Verhulst

Article by Emanuel Maiberg: “The report, titled “Are AI Bots Knocking Cultural Heritage Offline?” was written by Weinberg of the GLAM-E Lab, a joint initiative between the Centre for Science, Culture and the Law at the University of Exeter and the Engelberg Center on Innovation Law & Policy at NYU Law, which works with smaller cultural institutions and community organizations to build open access capacity and expertise. GLAM is an acronym for galleries, libraries, archives, and museums. The report is based on a survey of 43 institutions with open online resources and collections in Europe, North America, and Oceania. Respondents also shared data and analytics, and some followed up with individual interviews. The data is anonymized so institutions could share information more freely, and to prevent AI bot operators from undermining their counter measures.  

Of the 43 respondents, 39 said they had experienced a recent increase in traffic. Twenty-seven of those 39 attributed the increase in traffic to AI training data bots, with an additional seven saying the AI bots could be contributing to the increase. 

“Multiple respondents compared the behavior of the swarming bots to more traditional online behavior such as Distributed Denial of Service (DDoS) attacks designed to maliciously drive unsustainable levels of traffic to a server, effectively taking it offline,” the report said. “Like a DDoS incident, the swarms quickly overwhelm the collections, knocking servers offline and forcing administrators to scramble to implement countermeasures. As one respondent noted, ‘If they wanted us dead, we’d be dead.’”…(More)”

From Safer Cities to Healthier Lives: The Top 10 Emerging Technologies of 2025

Curated on Posted on by Stefaan Verhulst

World Economic Forum: “As cities become more connected, collaborative sensing is enabling vehicles, traffic systems and emergency services to coordinate in real time – improving safety and easing congestion. This is just one of the World Economic Forum’s Top 10 Emerging Technologies of 2025 that is expected to deliver real-world impact within three to five years and address urgent global challenges….The report outlines what is needed to bring them to scale: investment, infrastructure, standards and responsible governance, and calls on business, government and the scientific community to collaborate to ensure their development serves the public good.

Trajectory of emerging technologies in healthcare overtime.
Trajectory of emerging technologies in healthcare overtime.

This year’s edition highlights a trend towards technology convergence. For example, structural battery composites combine energy with storage design, while engineered living therapeutics merge synthetic biology and precision medicine. Such integration signals a shift away from standalone innovations to more integrated systems-based solutions, reshaping what is possible.

“The path from breakthrough research to tangible societal progress depends on transparency, collaboration, and open science,” said Frederick Fenter, Chief Executive Editor, Frontiers. “Together with the World Economic Forum, we have once again delivered trusted, evidence-based insights on emerging technologies that will shape a better future for all.”

The Top 10 Emerging Technologies of 2025

Trust and safety in a connected world:

1. Collaborative sensing

Networks of connected sensors can help vehicles, cities and emergency services share information in real time. This can improve safety, reduce traffic and respond faster to crises.

2. Generative watermarking

This technology adds invisible tags to AI-generated content, making it easier to tell what is real and what is not. It could help fight misinformation and protect trust online…(More)”.

Robodebt: When automation fails

Curated on Posted on by Stefaan Verhulst

Article by Don Moynihan: “From 2016 to 2020, the Australian government operated an automated debt assessment and recovery system, known as “Robodebt,” to recover fraudulent or overpaid welfare benefits. The goal was to save $4.77 billion through debt recovery and reduced public service costs. However, the algorithm and policies at the heart of Robodebt caused wildly inaccurate assessments, and administrative burdens that disproportionately impacted those with the least resources. After a federal court ruled the policy unlawful, the government was forced to terminate Robodebt and agree to a $1.8 billion settlement.

Robodebt is important because it is an example of a costly failure with automation. By automation, I mean the use of data to create digital defaults for decisions. This could involve the use of AI, or it could mean the use of algorithms reading administrative data. Cases like Robodebt serve as canaries in the coalmine for policymakers interested in using AI or algorithms as an means to downsize public services on the hazy notion that automation will pick up the slack. But I think they are missing the very real risks involved.

To be clear, the lesson is not “all automation is bad.” Indeed, it offer real benefits in potentially reducing administrative costs and hassles and increasing access to public services (e.g. the use of automated or “ex parte” renewals for Medicaid, for example, which Republicans are considering limiting in their new budget bill). It is this promise that makes automation so attractive to policymakers. But it is also the case that automation can be used to deny access to services, and to put people into digital cages that are burdensome to escape from. This is why we need to learn from cases where it has been deployed.

The experience of Robodebt underlines the dangers of using citizens as lab rats to adopt AI on a broad scale before it is has been proven to work. Alongside the parallel collapse of the Dutch government childcare system, Robodebt provides an extraordinarily rich text to understand how automated decision processes can go wrong.

I recently wrote about Robodebt (with co-authors Morten Hybschmann, Kathryn Gimborys, Scott Loudin, Will McClellan), both in the journal of Perspectives on Public Management and Governance and as a teaching case study at the Better Government Lab...(More)”.