Stefaan Verhulst
Article by Stefaan Verhulst and Artur Kluz: “The release of Pope Leo XIV’s new Encyclical Letter Magnifica Humanitas — On Safeguarding the Human Person in the Time of Artificial Intelligence marks an important intervention in one of the defining debates of our time: how humanity should govern and direct technological power of artificial intelligence in service of the human person. While much of the global press coverage has understandably focused on the document’s warnings regarding autonomous weapons systems and the urgent call to “disarm AI,” the encyclical is ultimately much broader and more ambitious. At its core, it is not simply a warning against dangerous technologies; it is a reflection about safeguarding the human person, the meaning of peace, human dignity, and the moral responsibilities that accompany technological development. As such, it contributes to a broader moral and normative understanding of why PeaceTech matters…(More)”.
Blog by Jack Strachan: “Over the last two decades, governments across the world have built hundreds of innovation labs, policy labs, behavioural insights teams and multidisciplinary transformation units. They emerged from a growing recognition that traditional institutional structures were struggling to respond to complexity, digital transformation and increasingly interconnected public problems. Labs offered a different organisational form: protected spaces within existing systems where smaller multidisciplinary teams could experiment outside normal bureaucratic logic.
And for a while, they worked remarkably well.
The early generation of public sector innovation labs genuinely changed government. Denmark’s MindLab helped bring ethnographic and participatory approaches into policymaking long before most governments were seriously talking about user-centred design. Helsinki Design Lab explored how strategic design could help states work across interconnected systems rather than departmental silos. Policy Lab UK experimented with multidisciplinary approaches inside Whitehall, while the early GDS movement in 2011 fundamentally reshaped expectations about what public digital services could be.
These were not vanity projects or post-it-note theatres. They produced rigorous work, brought new professions into government, changed the legitimacy of user-centred design inside the state and created new ways of understanding public problems. More importantly, they created conditions most institutions struggle to sustain – protected authority, proximity to decision-making, permission to experiment and the ability to generate evidence through building rather than reporting…
But over time, many of these environments lost the conditions that had made them effective in the first place…(More)”.
Article by the Australian Resilient Democracy Network: “…The civic life journey concept views the progression of an individual’s relationship with society through different stages of civic life. Whilst key stages of the journey are defined by age, they can also be differentiated across other factors and life experiences – such as where people live, experiences of disability or different cultural backgrounds.
The civic life course approach includes analysis of transition points in individual’s life course, such as from school into workforce or out of workforce. It also seeks to disaggregate preferences for when, how and where to engage. It seeks to use this analysis and framing to target programs and support to increase access to opportunities across each of the life course stages.
This Figure above presents a simplified model of the civic life course. It outlines three interacting elements of the civic life journey: civic literacy (knowledge and skills for democratic participation), civic participation (actions and behaviours that contribute to public life), and civic connection (belonging, agency, cohesion and responsibility developed through engagement).
We know from national surveys that meaningful civic engagement and education are protective factors against the declining trust in institutions and growing polarisation that liberal democracies including Australia are experiencing (see APSC Civic Education and Democratic Perceptions). While young people report sharply lower senses of belonging and Australia’s national standardised NAPLAN Civics and Citizenship exams show declining understanding of how our government works, more than half of Australians report feeling their voices are not heard in key public decisions.
But we don’t monitor these patterns or disentangle practical insights on when and where people prefer to engage, what access they have to opportunities, what barriers they face, and when civic engagement is most meaningful. Surveys suggest those who are active in their communities report higher trust and satisfaction with our democratic systems…(More)”
Article by Madeleine I. G. Daepp, Kiran Tomlinson, Scott Counts & Siddharth Suri: “Knowledge work has been key to economic flourishing in most advanced and many emerging economies in the last half century. Defined by the synthesis and creation of ideas rather than the production of physical goods, knowledge work involves the processing of non-routine problems that require judgment-based and creative intellectual capabilities. Such work is a large and important component of contemporary economies, accounting for an estimated third to half of all jobs in high-income countries and a fifth of all jobs globally. Achieving sustained economic growth increasingly depends on the ability to leverage and create knowledge, with countries actively seeking to transition to knowledge economies to improve their economic outcomes. Knowledge work is also the foremost application for which workers are using generative artificial intelligence (AI). A critical question for the future of twenty-first-century economies, then, is whether generative AI could democratize knowledge work by expanding the set of people who can engage in and benefit from it.
Generative AI’s effect on knowledge work hinges on emerging challenges along those two dimensions: (1) who benefits from AI’s use and (2) who actually uses AI. In this Perspective, we synthesize recent empirical work to map out these challenges and describe both technical and policy interventions to mitigate harms and ensure that benefits are widely shared. Technological and institutional fixes will need to be developed in tandem. Policies will need to be calibrated—towards either sharing productivity gains or building skills—according to what current models enable, and tooling will need to be made broadly usable if AI literacy and adoption pushes are to be effective in closing persistent participation gaps…(More)”.
Article by John Fell, Sándor Gardó, Domenic Kellner, Benjamin Klaus, Jan Hannes Lang, Lukas Nagy, Pucho Vendrell, Marek Rusnák, Jonas Wendelborn and Stefan Wredenborg: “Financial stability communication is challenging because its task is not to forecast financial crises, let alone predict their precise timing. Rather, it is to identify vulnerabilities and explain how the financial system is likely to fare should it be confronted with adverse shocks. Great care is needed in this endeavour, because the sentiment of financial stability communication can influence market perceptions and risk assessments, as well as broader economic and financial outcomes. Given the presence of this potential feedback loop, the task of financial stability communication at the ECB has long been guided by a broad concept of financial stability: the smooth allocation of financial resources, effective management of risk by financial institutions and the capacity of the financial system to absorb shocks. Using the messages conveyed in the ECB’s Financial Stability Review over two decades, this special feature compares dictionary-based, FinBERT and prompt-based AI approaches to extracting financial stability sentiment. It finds broad co-movement across methods, while the GPT-based filter isolates sentences that contain explicit risk assessments, capturing subtle shifts in tone and context that were previously difficult to quantify. Used carefully, such tools can support risk monitoring and drafting consistency over time, but they remain complementary to expert judgement, vulnerability analysis and stress testing, rather than substitutes for it. A deep-dive box in the special feature also shows how AI can be used to systematically extract information from financial news to create an indicator for the severity and probability of triggers (SPOT) for financial stability risks…(More)”.
Report by the Asia Society: “…surfaced nine factors that define the conditions that national strategies must get right to enable responsible and rapid AI adoption: trusted datasets, AI infrastructure, AI skills and awareness, global AI value chain leverage, ethical AI development, misinformation governance, AI governance frameworks and institutions, environmental sustainability, and cybersecurity. Each factor represents a domain in which the absence of measurable progress creates conditions that can stall adoption, erode public confidence, or concentrate power in ways that undermine the broader ecosystem. The following sections elaborate on each metric. The discussion is grounded in both policy analysis and stakeholder consultations, so that policymakers can identify where the most significant trust gaps lie.
The metrics proposed here are not yet available as standardized, comparable data across Asian economies. However, the policy analysis and stakeholder consultations reveal consistent patterns in legislation, governance commitments, institutional design, and stated priorities, providing a foundation from which measurement frameworks can be built. The value of naming these metrics lies precisely in making that construction a shared and deliberate project, rather than leaving it to occur ad hoc…(More)”.
Article by Pokere Paewai: “A new decentralised data storage network will put Māori data in Māori hands with the goal of ensuring Māori sovereignty doesn’t “stop at the server door”.
Designed by Te Kāhui Raraunga, Te Pā Tūwatawata will be available to marae, hapū, iwi or other organisations who wish to store their data within the protection of the Pā.
Principal advisor Erena Mikaere said it was a commercial storage service designed specifically to meet the needs of iwi Māori, hapū and marae.
The project was built on open source technology and led by Māori scientists, Māori engineers and grounded in tikanga Māori, she said.
“Central to everything from its architecture, to its initial conceptions, to the values that drive it, and then also to our customer service delivery, it’s really about doing things in a very Māori way, based on a Te Ao Māori worldview. And so to that end, we didn’t just want to offer like an automated store with us and push this button and register your name and company and here’s the invoice type of style. It starts with a conversation, it starts with a kōrero, like all good things. And so that means that we can provide them with a really tailored service.”
Te Pā Tūwatawata provides end-to-end encryption of data, both in transit and at rest, which Mikaere said would mean only the group who submitted the data to the platform would have the “keys” required to decrypt it.
“What it does is it provides a safe place for some of our data that we might consider, or that whānau and hapū, iwi might consider are some of our most sensitive sets of mātauranga. It provides a way in which we can protect that and ensure extra restriction, say over another data set, which perhaps isn’t as sensitive.”..(More)”.
Article by Rina Chandran: “Since OpenClaw burst onto the scene as Clawdbot last November, individuals and businesses have embraced artificial intelligence agents to write code, send emails, run a shop, and more. AI agents are forecast to become ubiquitous in the coming years, raising concerns about agentic inequality, and its economic consequences for companies, countries, and people.
AI agents are built on top of large language models, and can reason and take actions to complete tasks on behalf of users. They have been touted as a way to do repetitive and mundane tasks to free up workers’ time for higher-value activities. Many agents still fail at the most basic tasks, and some perform unauthorized actions, yet big tech firms including Google, Amazon, Anthropic, and Perplexity are launching agents that can do increasingly complex tasks autonomously.
As AI agents become more integrated into the economy, companies and entities that deploy them will benefit disproportionately compared to those that cannot, Nick Srnicek, a senior lecturer in digital economy at King’s College London, told Rest of World.
“We will see new inequalities of access, scale, quality and trust: divides between those who have agents and those who don’t; those who have good agents and those who have bad agents; those who have many agents and those who have few agents; and those who can trust their agents and those who cannot,” he said…(More)”.
Paper by Paris21: “…argues that the statistical community has reached a fork in the road. Incremental adjustment alone may no longer be sufficient. Countries and the international community face a strategic choice between two broad paths, each with distinct implications for legitimacy, financing, risk and equity. Rather than prescribing a single solution, the paper provides a framework to facilitate informed debate among all stakeholders of official statistics as they chart a course towards more sustainable and inclusive data systems. It is also the foundation for a forthcoming series of policy briefs that will explore specific aspects of this theme in greater depth and help sharpen policy attention to key areas.
This is a decisive moment for official statistical systems.
Deep cuts in development financing for statistics, legitimacy issues, artificial intelligence and other rapid technological changes, and rising expectations for more inclusive and participatory data are colliding with long-standing weaknesses in trust, capacity and data use. For many national statistical offices (NSOs), particularly in low- and middle-income countries, this convergence amounts to a systemic data crisis that threatens their relevance, credibility and sustainability. At the same time, these pressures create a rare opportunity to rethink how data systems are designed, governed and embedded in society…(More)”.
Article by Adele Peters: “Last October, days before Hurricane Melissa slammed into Jamaica, it wasn’t obvious how quickly the storm would intensify or the path it would take. But inside Google, an experimental AI model was spinning through dozens of scenarios, including the possibility that it might be the strongest hurricane on record to hit the island.
Five days before the storm made landfall—while traditional weather models were undecided on whether it would weaken and turn in another direction—the AI model, called WeatherNext, predicted with 80% confidence that Melissa would rapidly intensify from a Category 1 storm to a Category 5 and land in Jamaica. Google sent its predictions to the U.S.’s National Hurricane Center, which used the models to help make a record-breaking high-intensity forecast.
That early forecast “was critical,” says Evan Thompson, principal director of the Meteorological Service of Jamaica. “We want to get the information as soon as possible and then continuously drill that message to the public.” A Category 5 hurricane had never made landfall on the island. The weather office warned residents that anything they had experienced before “would pale in comparison,” Thompson says, and urged people to prepare however they could…
The Google DeepMind model was more accurate than any other model the National Hurricane Center used during the storm. Now, as the new hurricane season begins on June 1, the NHC will work with Google again. Last year, the model ran a set of 50 possible futures every six hours; this year, it will look at 1,000 futures every six hours, making it even more likely that it can predict unusual storms. “This significant increase should provide more stable and consistent guidance,” says Philippe Papin, NHC senior hurricane specialist.
Google is one of several companies working to use AI to reshape forecasting as weather becomes more extreme. That includes other large tech companies like Microsoft, Nvidia, and Huawei, and startups like Atmo, Tomorrow.io, and WindBorne—some of which are also collecting better data through cheap satellites or redesigned weather balloons…(More)”.