Stefaan Verhulst
Article by Sarah O’Connor: “Arguments about the past are often used as proxies for arguments about the future. It is no surprise, then, that a long-running debate about the Industrial Revolution has flared up as we begin another phase of rapid technological change.
The argument concerns whether the industrial revolution was good or bad for workers in the short run (and, by extension, which the AI revolution will be). The discourse in tech circles can be boiled down to this: “Relax: the industrial revolution led to higher real wages and more jobs”. “But don’t you know about ‘Engels’ pause’? Between 1790 and 1840, profits rose but real wages barely budged.” “Ah, but don’t you know that a different measure of real wages tells a different story?” And so on.
I find this argument perplexing — not because there are no lessons to be learnt from the industrial revolution, but because I don’t think these databases are the right place to look for them.
For one thing, the data on that era is patchy and unreliable. For another, the industrial revolution in Britain took place against a very different institutional backdrop. There was no universal suffrage, no legal trade unions and no modern welfare state. Indeed, you could argue these were eventual social responses to the industrial revolution. It is hard to see why we should expect the wage-setting dynamics of the past (even if we could agree on what they actually were) to repeat themselves today.
But most importantly, these quantitative metrics do not capture how profoundly the industrial revolution changed the nature of work for many people, in ways both good and bad. As the historian EP Thompson puts it in The Making of the English Working Class, “some of the most bitter conflicts of these years turned on issues which are not encompassed by cost-of-living series”: health, working hours, child labour, security and independence…(More)”.
Paper by Sara Thabit, Till Degkwitz, Mahardika Fadmastuti and Luca Mora: “Technology decentralisation is increasingly proposed as a key feature to build more trustworthy, accessible, and innovative digital public infrastructure, yet there is limited empirical knowledge of the actual benefits that such decentralised approaches would create from a public sector perspective. Furthermore, existing literature often relies on a linear dichotomy between data supply and demand that fails to capture the complexity of decentralised data ecosystems. This paper addresses these gaps by adopting an assemblage thinking perspective to conceptualise data platforms as complex socio-technical arrangements, and by developing a public values framework to broaden the understanding of the various outcome that data platforms can create. We apply this approach to an exploratory case study of Hamburg’s Urban Data Platform (UDP). Our findings demonstrate that public value creation is not determined by technical decentralisation alone but by specific architecture-governance configurations. We illustrate that both decentral and central practices can co-occur within the same system, where the role of a leading orchestrator is crucial to drive public value creation…(More)”.
Open Consultation by the Minister of State for Digital Government and Data: “People generate large volumes of personal data through everyday activities, yet they currently derive limited benefit from it. Instead, this data is held by data controllers, creating a clear power imbalance between individuals and those organisations that collect and control their data.
Data intermediaries offer a way to rebalance this relationship. They offer the potential to empower individuals to take control of their own data, operating as third parties to enable those individuals to better access, share and manage their personal data. The types of data intermediary are varied, but they generally allow users to either regain control of who can access their data, or to share their data on their own terms. By revolutionising where and by whom data is held, intermediaries can unlock new, unrealised benefits from people’s data, ranging from innovative personalised AI services to groundbreaking new research enabled fully by the user’s informed consent.
Although personal data is constantly produced, its value is still overwhelmingly captured by traditional data controllers rather than by the individuals who generate it. While people have rights as data subjects to access their data, these rights are often under-utilised. Our call for evidence last year found that people’s awareness of what they can do with their data through third parties is limited.
There is a huge opportunity for the UK to use data more strategically, to unlock stronger competition across markets, helping to stimulate innovation and deliver sustained economic growth. The government recognises that intermediaries can play a vital role in unlocking competitive data‑driven markets and supporting innovation, productivity and growth, and is committed to creating the conditions for the easy and secure sharing and reuse of high‑quality data that intermediaries enable. This is a nascent and rapidly developing area of activity in the UK and globally, offering significant potential to enhance outcomes for people, businesses and the wider economy.
Responses to our call for evidence last year indicated there are three things that need to be achieved for intermediaries to be able to function better in the UK: legal ambiguities need to be addressed; data controllers must be confident providing people’s data to another party; and user awareness of intermediaries and their potential value needs to grow. Currently, the barriers around these three areas are limiting the uptake and growth of the sector. Responses suggest these barriers appear interrelated and mutually reinforcing, leading to a sector with potential that is yet to be fully realised.
If the barriers to the sector are addressed, the potential is huge. What if, instead of a data controller typically deciding how your data is used, you were able to exercise greater control over your data and confidently determine who has access to it? Or if you could donate it to research projects to enable new discoveries? Or if it were powering personalised, innovative new AI services that could securely combine your data from multiple sources to provide you with new insights or suggestions? Data intermediaries are essential to unlocking this vision…(More)”.
Article by Kinling Lo: “For decades, the pilgrimage route for ambitious tech founders, investors, and engineers led to Silicon Valley. Now, a growing number of them are flying to Shanghai, Hangzhou, or Shenzhen instead. China is seeing a surge in a new kind of tech tourism where visitors pay up to $9,000 for curated tours of electric-vehicle factories, robotaxis, and artificial intelligence and robotics companies. The trend is partially triggered by viral videos of China’s dancing humanoid robots and flying cars, creating a sense that the country may be moving faster than the West in key emerging technologies. As China-U.S. tech competition intensifies, these trips are also becoming a means to explore the next investment opportunity and tech breakthrough.
“There’s a fear-of-missing-out dynamic at play: the sense that China’s tech ecosystem has reached a level of sophistication where not seeing it firsthand puts you at an informational disadvantage relative to competitors who have,” Shaoyu Yuan, an international relations adjunct professor at New York University who specializes in China’s soft power, told Rest of World…(More)”.
Paper by Lorenzo Gabrielli, Patrizia Sulis, Sara Thabit and Marco Minghini: “Open, non-governmental building datasets have become increasingly important for urban analysis, exposure modelling, and policy support. Despite their growing use, little is known about the consistency, completeness, and comparability of the semantic information they provide at a continental scale. This study presents the first systematic comparison of the semantic attributes of six major pan-European open building datasets—OpenStreetMap, EUBUCCO, Microsoft Global ML Building Footprints, Overture Maps, GHS-OBAT, and the Digital Building Stock Model (DBSM)—using the 27 EU Member States as a common reference area. Five key semantic attributes (height, typology, building age, number of floors, and building material) were harmonised and analysed in terms of completeness and value distributions across countries and degrees of urbanisation. The workflow combines API-based data ingestion, distributed geospatial processing, and high-performance computing to handle around 1.250 billion building footprints. Results reveal pronounced heterogeneity in semantic content across datasets. Remote-sensing-derived products (GHS-OBAT and DBSM) exhibit the highest levels of attribute completeness for height, typology, and building age, but rely on aggregated or coarse semantic representations. In contrast, community-driven and conflated datasets (OpenStreetMap and Overture Maps) provide richer and more detailed semantic schemas, albeit with low and spatially uneven completeness. Completeness patterns vary substantially across countries and urbanisation classes, and high completeness values often mask limited semantic informativeness due to the prevalence of unknown or aggregated attribute values. Overall, the findings demonstrate that no single dataset is universally optimal regarding consistency and completeness of building footprints’ semantic attributes. Nonetheless, the paper provides practical guidance for selecting suitable data sources depending on spatial scale, attribute requirements, and analytical objectives…(More)”.
Declaration developed by a community of mathematicians and adjacent researchers: “Technological developments have repeatedly transformed the practice of mathematics. Recent artificial intelligence technologies, including symbolic and neural methods for the generation and formalization of mathematics, may already have initiated a significant chapter in this long history. Among researchers, artificial intelligence has produced a wide range of reactions: enthusiasm for its potential to yield new discoveries; intimidation by the pace of developments; indifference to these rapid changes; and concern for the implications, both for mathematics and in wider society….Recent developments in artificial intelligence threaten each of these values, often in ways that disproportionately affect students and early-career mathematicians, and hence the long term future of the discipline.
Technologies which affect the way in which mathematics is practiced may disturb the current system of incentives. The use of artificial intelligence — and thus also the sort of problems which it can address — may become incentivized for its own sake, disrupting our mechanisms for hiring, funding, and recognition. This disadvantages researchers who do not have access to the technologies or decision-making related to them, or who are unwilling to use technologies controlled by organizations whose values they do not share.
Current automated techniques can produce plausible but unreliable (or even incorrect) arguments which are difficult to distinguish from correct mathematical proofs. This applies not only to informal arguments, but also to formalizations, where the difficulty lies in the translation between computer-encoded and human presentations of concepts. These fast-moving developments put our present system of review under increasing pressure, jeopardizing our ability to implement traditional standards for the correctness, transparency, and independent verifiability of proof.
Technologies that draw extensively on the published mathematical commons undermine the traditional system of attribution. Models trained on published works frequently return outputs that do not properly cite the human works they synthesize. Many current models are also built on data obtained by systematically exploiting licenses and access arrangements that were not made with artificial intelligence in mind, or indeed by simply violating copyright protections…(More)”.
Book by Andrew Lison: “Since the end of the Second World War, we have come to expect continual growth in computing power and the rapid development of digital technology. This dynamic has enabled informational procedures to supplant an ever-increasing range of human and mechanical activity. However, indications that the semiconductor industry is approaching the physical limits of integrated circuitry pose an existential challenge to Intel Corporation cofounder Gordon Moore’s “law,” which prescribes an exponential increase in microchip density—and, by extension, processing performance—every two years.
Placing theories of employment in dialectical conjunction with the concrete operations of computing, 100% Utilization explores the consequences of pushing processing power to its limits for a culture seemingly reliant on automation as much as human labor. In accounting for this contradiction, Andrew Lison offers a corrective to theories of digital mediation, emphasizing its symbolic and representational capabilities. He connects the looming end of Moore’s law to trends in semiconductor manufacturing, custom hardware, and parallelized software techniques, including AI. Ultimately, he traces this historical technological boom and impending bust through the racialized history of Silicon Valley to longer-term conceptions of the relationship between machinery and labor…(More)”.
Book by James M. Tabor: “In 1854, the American entrepreneur Cyrus Field set out to lay a 2,000-mile telegraph cable across the bottom of the Atlantic Ocean. Nothing like it had ever been attempted. Field knew nothing about telegraphy, electricity, ships, or oceans, and science itself still lacked a universal theory of electricity. But he believed that wiring the world for near-instantaneous communication would bring about peace on Earth. In 1866, after enduring over a decade of global scorn, catastrophic failures, staggering losses, and brushes with death, he would finally lay his great cable, ushering in the global information age. From acclaimed author James M. Tabor, Lightning Beneath the Sea is an unforgettable tale of radical vision, unwavering determination, and triumph against overwhelming odds that transformed life on Earth forever.
In a propulsive narrative, Tabor tells how Field swiftly assembled an all-star scientific dream team that included telegraph legend Samuel F. B. Morse; a young Lord Kelvin, called the da Vinci of his day; Michael Faraday, the father of electrical engineering; and legendary philanthropist Peter Cooper. Together they battled epic storms, freak accidents, corporate sabotage, the enmity of Abraham Lincoln, and the hubris of the project’s original chief electrician—an eccentric who insisted on being called Wildman—while racing two rival efforts to establish telegraphic communications between continents. When it was finally done, Field’s cable lay up to 2.5 miles deep under the ocean, and the London Daily News announced: “Time and space seem literally annihilated.” The cable’s legacy can be traced today in the hundreds of descendants that still carry 98 percent of the world’s information through a “world undersea web.”…(More)”
Paper by Marcia Langton, Robert McLellan, Jennifer Fewster, and Kristen Smith: “The Framework for the Governance of Indigenous Data has been developed to guide ethical, inclusive, and culturally grounded data practices across the Humanities, Arts, Social Sciences, and Indigenous Research Data Commons (HASS and Indigenous RDC).
It responds to long-standing calls from Aboriginal and Torres Strait Islander communities for greater control over data that affects their lives, lands, cultures, and futures. The Framework is the result of extensive consultation, co-design, and collaboration with Indigenous data custodians, researchers, institutions, and policymakers. It is intended to be adopted across all HASS and Indigenous RDC activities.
This Framework affirms that Indigenous data governance is essential to Indigenous self-determination. It defines Indigenous data as information generated by, about, or for Aboriginal and Torres Strait Islander peoples, encompassing cultural, ecological, linguistic, genealogical, and community-generated knowledge. The Framework recognises that all data – whether held by governments, institutions, or communities – has implications for Indigenous peoples and must be governed in ways that respect Indigenous rights, laws, and relational worldviews.
The Framework is structured around three interrelated components: a Governance Model, a set of Governance Guidelines, and a Monitoring and Accountability structure. The Governance Model identifies 5 foundational elements:
- Recognition of Indigenous assets
- Partnership
- Building capabilities
- Self-determination
- Inclusive data ecosystem.
These elements underpin the Framework’s guidelines and practices, which provide actionable strategies for institutions and communities to embed Indigenous governance across the data lifecycle.
The Framework is grounded in the CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics) and complements the FAIR Principles (Findable, Accessible, Interoperable, and Reusable)…(More)”.
Article by Ashley Farley: “When the Gates Foundation’s Open Access Policy was established, in 2015, the aim was to shift the ecosystem so that open access became the norm. In the years that followed, while open access output did increase, the overall ecosystem shifted in the wrong direction: costs rose sharply and commercial capture intensified.
In 2024, as the foundation reflected on what about the policy was working and what could be improved, we had two choices: to stay the course with a policy that wasn’t improving the broader ecosystem or to cease support for unsustainable practices. We chose to confront the systemic issues in open access publishing head-on, updating our policy to require Gates-funded research be shared as preprints. This change enabled the foundation to divest from restrictive article processing charges (APCs) and redirect resources toward more equitable and sustainable open access models….As APCs became the dominant model for open access publishing, they crowded out other, more equitable approaches and made it difficult for alternative models to scale. The foundation’s decision to stop paying APCs will inevitably put pressure on the broader publishing ecosystem, with both positive and challenging effects.
While the intention is to build a more equitable open access system, we recognize that authors without access to alternative funding for APCs will face tougher publishing choices in the short term. But our data shows that grantees continue to find ways to pay for APCs. And the new policy intentionally preserves author choice. Authors are not required to choose journals that levy APCs. As the various financial streams that flow to publishers start to consolidate, that flow could better reflect the values of funders and institutions, creating pressure that encourages publishers—especially large commercial ones—to reduce their prices. Although publishing carries real costs, charging researchers thousands of dollars to make their work openly available is rooted in inequitable, colonial structures…(More)”.