Stefaan Verhulst

Blog by Rainer Kattel: “The UK government published last week the Public Design Evidence Review (PDER), an ambitious attempt to answer a deceptively simple question: How do we create better public policies and services that consistently achieve their intended outcomes? One of the answers, the report argues, lies in public design — a term the report introduces… public design fundamentally challenges modernist assumptions about how governments should work: it questions and expands the idea that politics is about representation and that bureaucracy is about neutral expertise. Instead, it imagines governance as a dynamic, participatory, and creative process, as summarised in the figure below from the PDER report.

Despite these promising ideas and examples, public design remains underdeveloped as a system-wide public practice. Evidence is often limited to individual case studies, with few robust measures of impact — especially on systemic change. There are brilliant cases like Dan Hill’s work Swedish innovation agency, Vinnova. But mostly design roles are still not embedded across the civil service. Toolkits are scattered. Teams often lack shared job descriptions or metrics to evaluate success.

That’s why the Public Design Evidence Review is so important. It systematises the scattered evidence, identifies promising practices, and points toward what needs to change.

To make public design transformative, we need to learn from the digital transformation journey. That means:

• Standardising design roles in government job descriptions and team structures
• Scaling access to design toolkits across departments and agencies
• Measuring impact not just in outputs but in terms of systemic change, dynamic capabilities, and long-term value creation…(More)”.

Article by Eric Mosley: Every organization wants better people data. This information about employee satisfaction and engagement is often used by organizations to assess and improve company culture. But how does the way we collect people data affect its ultimate value to the organization?

In the race to use artificial intelligence (AI), many organizations have defaulted to a familiar mindset around data: Collect everything and sort it out later. But most Americans are uneasy about how companies use their data and are resigned to feeling that they’ve lost control, according to a Pew Research Center survey. And nearly 68% of consumers globally say they are either somewhat or very concerned about their privacy online. These kinds of feelings are dangerous because trust evaporates when people feel watched rather than respected.

From quiet monitoring to inferred behaviour, the rise of passive data mining is triggering a backlash. Some people are setting their own boundaries by asking companies not to track their clicks, mine their Slack or email messages, or make their data part of the company’s algorithm without consent.

If we want people to trust AI systems – or the organizations building them – we need to start with data practices that earn that trust. That means moving from pure extraction to something more cooperative, human and voluntary…(More)”.

Book by David W. Galenson: “When in their lives are innovators most creative and why? This book summarizes more than two decades of research prompted by this question. The result is an authoritative statement of a new unified theory of creativity that overturns both popular and scholarly beliefs about the sources of human inventiveness. David Galenson shows that there are two distinctly different kinds of creativity in virtually every discipline. They result from very different goals and methods, and each produces a specific pattern of discovery over the life cycle. Conceptual innovators make bold leaps to formulate new ideas. The most radical conceptual innovations are made by brash young geniuses, who often lose their creativity thereafter. Great conceptual innovators analyzed in this book include Pablo Picasso, Albert Einstein, Orson Welles, Sylvia Plath, Andy Warhol, Bob Dylan, and Steve Jobs. Experimental innovators make discoveries gradually and unobtrusively, through careful observation and generalization. They gain knowledge over time and make their greatest contributions late in their lives. Great experimental innovators considered in this book include Paul Cézanne, Charles Darwin, Virginia Woolf, Robert Frost, Alfred Hitchcock, John Coltrane, and Warren Buffett. From analysis of the careers of scores of artists, scholars, and entrepreneurs, this book provides a new understanding of the creative processes of great innovators and reveals the systematic patterns that underlie the two life cycles of creativity. It will be of interest to anyone who seeks a deeper understanding of the sources of human creativity…(More)”.

Blog by Qhala: “…In AI, benchmarks are the gold standard for evaluation. They are used to test whether large language models (LLMs) can reason, diagnose, and communicate effectively. In healthcare, LLMs are tested against benchmarks before they’re considered “safe” for clinical use.

But here’s the problem: These benchmarks are primarily built for Western settings. They reflect English-language health systems, Western disease burdens, and datasets scraped from journals and exams thousands of kilometres away from the real-world clinics of Kisumu, Kano, or Kigali.

A study in Kenya found over 90 different clinical guidelines used by frontline health workers in primary care. That’s not chaos, it’s context. Medicine in Africa is deeply localised, shaped by resource availability, epidemiology, and culture. When a mother arrives with a feverish child, a community nurse doesn’t consult the United States Medical Licensing Examination (USMLE). She consults the local Ministry of Health protocol and speaks in Luo, Hausa, or Amharic.

In practice, Human medical Doctors have to go through various levels of rigorous, context-based, localised assessment before they can practise in a country and in a specific specialisation. These licensing exams aren’t arbitrary; they’re tailored to national priorities, clinical practices, and patient populations. They acknowledge that even great doctors must be assessed in context. These assessments are mandatory and are an obvious logic when it comes to human clinicians. A Kenyan-trained doctor must pass the United States Medical Licensing Examination (USMLE). In the United Kingdom, it is the Professional and Linguistic Assessments Board (PLAB) test. In Australia, the relevant assessment is the Australian Medical Council (AMC) examination.

However, unlike the nationally ratified assessments for humans, the LLM benchmarks and subsequently the LLMs and Health AI tools are not created for local realities, nor are they reflective of the local context.

…Amidst the limitations of global benchmarks, a wave of important African-led innovations is starting to reshape the landscape. Projects like AfriMedQA represent some of the first structured attempts to evaluate large language models (LLMs) using African health contexts. These benchmarks thoughtfully align with the continent’s disease burden, such as malaria, HIV, and maternal health. Crucially, they also attempt to account for cultural nuances that are often overlooked in Western-designed benchmarks.

But even these fall short. They remain Anglophone…(More)”.

British Council: “From telling stories that seed future breakthroughs to diversifying AI datasets, artists reimagine what technologies can be, and who they can be for. This publication creates an international evidence base for this argument. 56 leaders in art and technology have offered 40 statements, spanning 20 countries and 5 continents. As a collection, they articulate artists, the cultural sector and creative industries as catalysing progressive innovation with cultural diversity, human values, and community at its core.     

Responses include research leads from Adobe, Lelapa AI and Google, who detail the contribution artists make to the human-centric development of high-growth technologies. UK institutions like Serpentine and FACT, and LAS Art Foundation in Germany show cultural organisations are essential spaces for progressive artist-led R&D. Directors of TUMO Centre for Creative Technologies in Armenia, and Diriyah Art Futures in Saudi Arabia highlight education across art and technology as a source of skills for the future. Leaders of African Digital Heritage in Kenya and the Centre for Historical Memory in Colombia demonstrate how community ownership of technologies for heritage preservation increases network resilience. Artists such as Xu Bing in China and Libby Heaney in the UK present art as a site for public demystification of complex technologies, from space satellites to quantum computing.    

The perspectives presented in this publication serve as a resource for policy making and programme development spanning art and technology. Global in scope, they offer case studies that highlight why innovation needs artists, on both a national and international scale…(More)”.

Paper by Alex Fischer et al: “While the Sustainable Development Goals (SDGs) were being negotiated, global policymakers assumed that advances in data technology and statistical capabilities, what was dubbed the “data revolution”, would accelerate development outcomes by improving policy efficiency and accountability. The 2014 report to the United Nations Secretary General, “A World That Counts” framed the data-for-development agenda, and proposed four pathways to impact: measuring for accountability, generating disaggregated and real-time data supplies, improving policymaking, and implementing efficiency. The subsequent experience suggests that while many recommendations were implemented globally to advance the production of data and statistics, the impact on SDG outcomes has been inconsistent. Progress towards SDG targets has stalled despite advances in statistical systems capability, data production, and data analytics. The coherence of the SDG policy agenda has undoubtedly improved aspects of data collection and supply, with SDG frameworks standardizing greater indicator reporting. However, other events, including the response to COVID-19, have played catalytic roles in statistical system innovation. Overall, increased financing for statistical systems has not materialized, though planning and monitoring of these national systems may have longer-term impacts. This article reviews how assumptions about the data revolution have evolved and where new assumptions are necessary to advance the impact across the data value chain. These include focusing on measuring what matters most for decision-making needs across polycentric institutions, leveraging the SDGs for global data standardization and strategic financial mobilization, closing data gaps while enhancing policymaker analytic capabilities, and fostering collective intelligence to drive data innovation, credible information, and sustainable development outcomes…(More)”.

Responsible Data for Children (RD4C): “From schools to clinics to the phones in their hands, children are generating more data than ever before. This data holds enormous potential, both informing smarter policies and helping every child to thrive. But with this opportunity comes serious risks, too. Misuse, breaches, and privacy violations are all too common. Without strong governance, the very systems meant to support children can expose them to harm, bias, or exclusion.

Since 2019, the Responsible Data for Children (RD4C) initiative—a partnership between UNICEF and The GovLab at New York University—has worked to strengthen and promote responsible data practices for and about children, from collection to processing to use—across every stage throughout the entire data lifecycle.

In this time, RD4C.org has reached more than 10,000 users across 165 countries, with its tools and resources viewed over 74,000 times—a reflection of growing global momentum to make data governance work better for children.

We are now pleased to announce the launch of an upgraded RD4C.org: a more accessible, dynamic, and action-driven platform to support responsible data use for and about children in today’s rapidly evolving digital landscape.

What’s New

RD4C.org has been upgraded with a fresh design and new features to scale impact, deepen accessibility, and better equip those working to uphold children’s rights in the rapidly evolving digital age.

• Multilingual Access: The upgraded RD4C.org is now available in five languages — English, Spanish, French, Arabic, and Chinese. By making the site and its resources accessible in multiple languages, RD4C empowers practitioners, policymakers, and advocates including children and young people to adapt and apply child-centered data governance principles across diverse political, cultural, and operational contexts.
• Comprehensive Resource Hub: The redesigned resource section brings together videos, case studies, and RD4C tools in one cohesive space. This enhanced collection page offers practical, actionable insights for anyone working to advance child-centered data governance, whether shaping national policies, improving service delivery, or designing ethical data systems.
• A New Editorial Space for Global Commitment: As part of our deepening commitment to cross-sector collaboration, RD4C.org now hosts a dedicated space spotlighting the Commitment to Data Governance Fit for Children—a global initiative launched at the 2024 UN World Data Forum to co-develop responsible data systems with and for children, grounded in their rights and realities. This new editorial focus featured in the blog section highlights practical insights from both young people and key committed partners — including UNICEF, the Govlab, GPSDD, the Datasphere Initiative, the Abu Dhabi Early Childhood Authority, Highway Child, Develop Metrics, and others — showcasing real-world efforts to make data governance truly fit for children…(More)”.

Briefing for European Parliament: “…explores the potential of generative AI in supporting foresight analysis and strategic decision-making. Recent technological developments promise an increased role for large language models (LLMs) in policy research and analysis. From identifying trends and weak signals to fleshing out rich scenario narratives and bringing them to life in experiential and immersive ways, generative AI is empowering foresight analysts in their endeavour to anticipate uncertainties and support policymakers in preparing better for the future. As generative agents powered by LLMs become more adept at mimicking human behaviour, they could offer foresight practitioners and policy analysts new ways to gain additional insights at greater speed and scale, supporting their work.

However, to effectively integrate generative AI and LLMs into foresight practice, it is crucial to critically evaluate their limitations and biases. Human oversight and expertise are essential for ensuring the reliability and validity of AI-generated outputs, as well as the need for transparency, accountability, and other ethical considerations. It is important to note that, while generative AI can augment human capabilities, it should not be seen as a replacement for human involvement and judgment.
By combining human expertise with generative AI capabilities, foresight analysts can uncover new opportunities to enhance strategic planning in policymaking. A proactive and informed approach to adopting generative AI in foresight analysis may lead to more informed, nuanced, and effective strategies when dealing with complex futures…(More)”.

Paper by Marcel Binz: “Establishing a unified theory of cognition has been an important goal in psychology. A first step towards such a theory is to create a computational model that can predict human behaviour in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language. We derived Centaur by fine-tuning a state-of-the-art language model on a large-scale dataset called Psych-101. Psych-101 has an unprecedented scale, covering trial-by-trial data from more than 60,000 participants performing in excess of 10,000,000 choices in 160 experiments. Centaur not only captures the behaviour of held-out participants better than existing cognitive models, but it also generalizes to previously unseen cover stories, structural task modifications and entirely new domains. Furthermore, the model’s internal representations become more aligned with human neural activity after fine-tuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behaviour across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories, and we present a case study to demonstrate this…(More)”.

Book by Robert V. Moody, Ming-Dao Deng: “One of life’s most fundamental revelations is change. Presenting the fascinating view that pattern is the manifestation of change, this unique book explores the science, mathematics, and philosophy of change and the ways in which they have come to inform our understanding of the world. Through discussions on chance and determinism, symmetry and invariance, information and entropy, quantum theory and paradox, the authors trace the history of science and bridge the gaps between mathematical, physical, and philosophical perspectives. Change as a foundational concept is deeply rooted in ancient Chinese thought, and this perspective is integrated into the narrative throughout, providing philosophical counterpoints to customary Western thought. Ultimately, this is a book about ideas. Intended for a wide audience, not so much as a book of answers, but rather an introduction to new ways of viewing the world.

• Combines mathematics and philosophy to explore the relationship between pattern and change
• Uses examples from the world around us to illustrate how thinking has developed over time and in different parts of the world
• Includes chapters on information, dynamics, symmetry, chance, order, the brain, and quantum mechanics, all introduced gently and building progressively toward deeper insights
• Accompanied online by additional chapters and endnotes to explore topics of further interest..(More)”.