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Stefaan Verhulst

Book by Ronald Bradfield: “Organizations today face an increasingly complex contextual environment. The intensity of what is recognized as a VUCA world has changed how they view the world, interact with each other, and respond to this environment.

Understanding the Future shows individuals and organizations how to develop scenario planning, using the Intuitive Logics (IL) model, to perceive what is happening in the business environment and how to improve strategic decision-making to plan for uncertainty.

Ronald Bradfield, a renowned scenario planning practitioner, traces the origins of scenario planning from its evolution to associated techniques and details the IL development process from Stage 1 to Stage 5. He includes an insightful chapter on how people think, describing the role of heuristics and biases, reviewing some of the commonly known ones, and concludes with the pros and cons of the IL model.

This book includes extensive reference material: appendices, a list of Foresight and Scenario organizations, Futures journals and magazines, published scenarios, select readings and guides, and the author’s unique case material directly from his world-leading consulting work of the past 30 years.

Understanding the Future is an exceptional, comprehensive guide for postgrads, practitioners, leaders, policymakers and anyone involved in organizational development or management risk who needs to understand the IL scenario framework and its value in addressing organizational challenges amidst complexity…(More)”.

Understanding the Future

Book by Luca Mora et al: “…explores how to govern the planning, implementation, and maintenance operations in smart city projects and transitions, and the urban digitalization processes that may potentially trigger. It provides readers with the evidence-based knowledge they need to approach the complexity of smart city governance, responding to the United Nations’ call for more advanced strategic and technical support on urban digital transformations to national, regional, and local governments. The book uses a comprehensive framework that details what configuration of multi-level components should be considered in the governance of smart city transitions…(More)”.

Smart City Code: Governance Handbook for Digital Transformation Managers in the Public Sector

Book by Mark Findlay,: “This prescient book examines social ordering and governance in the digital universe. It demonstrates how attempts to enact regulations in virtual spaces cannot replicate laws and market arrangements in the real world, advocating for an alternative ‘new law’ to enable safe, sustainable and beneficial digital communities.

Mark Findlay discusses how this ‘new law’ could be achieved and the challenges it might face, addressing ideas of inclusive and collaborative governance, digital self-determination and communal bonding in virtual spaces. He outlines the differences between the metaverse and ‘realspace’ which call into question conventional legal reasoning and appreciations, rethinking current reductive paradigms of the virtual universe as an outpost for private property and exchange markets. Governing the Metaverse ultimately explores the notion of law as an enabler of change rather than an enforcer of the status quo and emphasises the potential of this more adaptable interpretation of the law to create an empowering digital world.

Students and scholars of constitutional and administrative law, law and politics and internet and technology law will greatly benefit from this thought-provoking book. It is also a vital resource for policymakers and practitioners in the fields of public policy, regulation and governance and technology and IT…(More)”.

Governing the Metaverse

Essay by Marthe Smedinga, Angela Ballantyne and Owen Schaefer: “Advancing the public interest’ is a criterion for de-identified data use for research via several national data platforms and biobanks. This may be referred to via cognate terms such as public benefit, public good or social value. The criterion is often adopted without it being a legal requirement. It is a legal requirement in some jurisdictions for sharing identifiable data without consent, which does not apply to de-identified data. We argue that, even in circumstances where there are few or no legal restrictions on the sharing of de-identified data, there is a sound ethical reason for platforms to nevertheless impose a public interest criterion on data sharing. We argue that a public interest test is ethically essential for justifying research use of de-identified data via government-funded platforms because (1) it allows to promote public good and to minimise potential harmful consequences of research for both individuals and groups, for example, by offering grounds to reject research that could lead to stigmatisation of marginalised populations; (2) national data platforms hold public data and are made possible by government funds, and therefore should be used to support public interests and (3) it can demonstrate trustworthiness and contribute to promoting the social licence for data platforms to operate, which is especially important for efforts to align data governance policies with public norms and expectations…(More)”.

Why de-identified data sharing for research should be in the public interest

Press Release: “On Saturday, 9 August, National Women’s Day, the Department of Planning, Monitoring and Evaluation (DPME), together with the Pan African Collective for Evidence (PACE), unveiled South Africa’s first AI-driven Living Evidence Map aimed at tackling Gender-Based Violence and Femicide (GBVF).

Minister Maropene Ramokgopa emphasised that the launch goes beyond mere technology—it’s about taking action. “It is a tool for action, a tool for justice, and a tool that puts survivors first. We will not end GBVF with words alone. We need evidence, accountability, and the courage to act.

The cutting-edge digital platform was developed to bolster Pillar 6 of the National Strategic Plan on Gender-Based Violence and Femicide (NSP GBVF), which focuses on improving research and information management.

With this initiative, South Africa now boasts the country’s largest centralised and regularly updated gender evidence database, consolidating academic research, community insights, evaluations, and government data into a single platform.

“To end GBVF, we need to understand what works, for whom, and why. This platform gives us the power to base decisions on evidence rather than assumptions,” stated the department.

The Living Evidence Map is a collaborative effort involving researchers, civil society, and government departments. It is powered by ChatEIDM, an AI engine that enables real-time interaction with the data.

The platform is designed to assist:

  • Policymakers in creating targeted interventions
  • Civil society organisations in developing evidence-based strategies
  • Researchers and evaluators in spotting gaps and trends
  • The general public in gaining insight into the scale of GBVF and potential solutions…(More)”
South Africa launches AI-powered Living Evidence Map to combat GBVF

Article by Haishan Fu, Aivin Solatorio, Olivier Dupriez and Craig Hammer: “AI, particularly large language models (LLMs), is completely transforming the way people interact with data. Data users at all levels of experience and expertise—from first-timers to power users—are now able to pose complex questions in natural language to chatbots, to which they expect to promptly find, interpret, and present data-driven insights packaged as pithy, accurate responses.

For this evolution to be successful, AI systems need to get it right. This means the data being accessed and interpreted by AI systems must first be evaluated, validated, structured, governed, and shared in ways that support the responsible and effective use of AI. In short, the data must be “AI-ready.” 

AI-ready data does not supplant earlier advancements, foundational concepts, or standards—such as the Fundamental Principles of Official Statistics, open data frameworks, or the FAIR (Findable, Accessible, Interoperable, and Reusable) principles—but rather it builds on them. By extending established foundations and standards, AI-ready data means that development data is continuously open, discoverable, and reusable, while ensuring that it is systematically organized and well-documented, to facilitate seamless use by both people and AI systems. Ensuring AI-readiness can thus shorten the distance between development data and decision-making for better policies and faster innovation, democratizing development insights. The World Bank, in its efforts to become a bigger, better “Data Bank,” is already working to make this happen, in partnership with country partners and the global development community…(More)” See also: Moving Toward the FAIR-R principles: Advancing AI-Ready Data.

From open data to AI-ready data: Building the foundations for responsible AI in development

Paper by Alisha Suhag, Romana Burgess and Anya Skatova: “The growing ubiquity of digital footprint data presents new opportunities for behavioral epidemiology and public health research. Among these, supermarket loyalty card data—passively collected records of consumer purchases—offer objective, high-frequency insights into health-related behaviors at both individual and population levels. This paper explores the potential of loyalty card data to strengthen public health surveillance across 4 key behavioral risk domains: diet, alcohol, tobacco, and over-the-counter medication use. Drawing on recent empirical studies, we outline how these data can complement traditional epidemiological data sources by improving exposure assessment, enabling real-time trend monitoring, and supporting intervention evaluation. We also discuss critical methodological challenges, including issues of representativeness, data integration, and privacy, as well as the need for robust validation strategies. By synthesizing the current evidence base and offering practical recommendations for researchers, this paper highlights how loyalty card data can be responsibly leveraged to advance behavioral risk monitoring and support the adaptation of epidemiological practice to contemporary digital data environments…(More)”.

Shopping Data for Population Health Surveillance: Opportunities, Challenges, and Future Directions

Paper by Elena Murray, Moiz Raja Shaikh, Stefaan Verhulst, Hinali Dosh, Romeo Leapciuc, Perizat Mamutalieva, and Mahadia Tunga: “As data-driven service delivery expands, data reuse holds significant potential to improve access to and quality of essential services for young people. However, limited youth involvement in decisions about how their data is reused risks perpetuating mistrust and deepening the inequalities that these services seek to address, particularly if young people choose to avoid seeking services or withhold critical information out of fear of misuse. Responsible data reuse to enhance service delivery must therefore be grounded in methodologies that meaningfully engage youth and reflect their preferences and expectations. This paper presents findings from the NextGenData project, which developed and piloted a scalable methodology for engaging young people aged 19-24 in co-designing responsible data reuse strategies. Conducted as a year-long participatory action research initiative across India, Tanzania, Moldova, and Kyrgyzstan, the approach implemented youth assemblies, deliberative methods, and localized facilitation by national partners to engage young people. The study emphasizes the importance of context-specific, culturally responsive facilitation, and sustained, multi-phase engagement as the foundation for establishing a social license for data reuse. We present recommendations for practitioners to embed youth-centered approaches in data governance and offer a publicly available toolkit for replication. By centering young people in data decisions, this methodology advances ethical, inclusive, and effective service delivery and digital self-determination for young generations…(More)”.

Who Decides What and How Data is Re-Used? Lessons Learned from Youth-Led Co-Design for Responsible Data Reuse in Services

Article by The Financial Times Editorial Board: “Pity anyone tasked with delivering bad news about the US economy to Donald Trump. For months, Federal Reserve chair Jay Powell has drawn the president’s ire by failing to engineer cuts to interest rates — prompting childish name-calling and threats to his job. On Friday, it was the Bureau of Labor Statistics’ turn. The agency published sluggish non-farm payroll numbers for July, and reduced its estimates for job creation in the prior two months by a chunky 258,000. Erika McEntarfer, the agency’s commissioner, was spared the insults only to be fired on the spot. A replacement is expected to be announced soon.

Trump claimed, without evidence, that McEntarfer massaged the figures. The most likely explanation is that the US president simply did not like the numbers. It was only a matter of time before the administration’s cuts to civil service jobs, downbeat surveys of private sector hiring plans and the strain of elevated interest rates showed up in the headline numbers. And although last week’s data downgrades were large, non-farm payrolls are notoriously volatile and revisions are common. The president said: “Important numbers like this must be fair and accurate, they can’t be manipulated for political purposes.” Yet by sacking the BLS chief on dubious grounds, he has undermined trust in America’s economic data, and politicised it.

First, the drastic move creates a culture of fear around the production of national economic statistics. This gives investors, businesses and the Fed reason to doubt whether concerns around a presidential backlash might influence forthcoming data releases not just from the BLS but also from other public bodies, including the Bureau of Economic Analysis, which produces the GDP numbers. Second, it is likely that Trump’s replacement for McEntarfer might be more pliant to his demands. That threatens the integrity of the BLS’s data itself, not just how it is perceived.

The president’s actions are unhelpful for his own ambitions too. The BLS produces reports on the labour market and inflation, which underpin the pricing of trillions of dollars in assets globally. While private data sources can plug some gaps, stoking doubts over the credibility of national data still erodes the ability of investors, businesses and policymakers to make informed decisions. Ironically, the central bank is looking for clear signs of weakness in the labour market before making the rate cuts that Trump so desires. Just as worrying is the imminent replacement of Fed governor Adriana Kugler — who, on Friday, stepped down early — with what Trump hopes will be a puppet rate-setter.

The BLS is not without flaws. Like many national statistics bodies, it has faced falling survey response rates, especially since the pandemic. This has raised questions over the representativeness of its samples and the accuracy of its aggregations. A funding squeeze — exacerbated by the Trump administration’s own public sector cutbacks — hasn’t helped. In February, several advisory councils to federal statistical agencies were also terminated. Rather than engaging in a useful revamp of national statistics, Trump has gone for the heavy-handed option.

The president isn’t alone. He joins a list of leaders, including from Turkey, Iran, Russia, Argentina and China, accused in recent years of meddling with economic institutions in order to control the public narrative…(More)”

Trump’s chilling assault on economic data

Article by Cade Metz: “In downtown Berkeley, an old hotel has become a temple to the pursuit of artificial intelligence and the future of humanity. Its name is Lighthaven.

Covering much of a city block, this gated complex includes five buildings and a small park dotted with rose bushes, stone fountains and neoclassical statues. Stained glass windows glisten on the top floor of the tallest building, called Bayes House after an 18th-century mathematician and philosopher. Lighthaven is the de facto headquarters of a group who call themselves the Rationalists. This group has many interests involving mathematics, genetics and philosophy. One of their overriding beliefs is that artificial intelligence can deliver a better life if it doesn’t destroy humanity first. And the Rationalists believe it is up to the people building A.I. to ensure that it is a force for the greater good.

The Rationalists were talking about A.I. risks years before OpenAI created ChatGPT, which brought A.I. into the mainstream and turned Silicon Valley on its head. Their influence has quietly spread through many tech companies, from industry giants like Google to A.I. pioneers like OpenAI and Anthropic.

Many of the A.I. world’s biggest names — including Shane Legg, a co-founder of Google’s DeepMind; Anthropic’s chief executive, Dario Amodei; and Paul Christiano, a former OpenAI researcher who now leads safety work at the U.S. Center for A.I. Standards and Innovation — have been influenced by Rationalist philosophy. Elon Musk, who runs his own A.I. company, said that many of the community’s ideas align with his own.

Mr. Musk met his former partner, the pop star Grimes, after they made the same cheeky reference to a Rationalist belief called Roko’s Basilisk. This elaborate thought experiment argues that when an all-powerful A.I. arrives, it will punish everyone who has not done everything they can to bring it into existence.

But these tech industry leaders stop short of calling themselves Rationalists, often because that label has over the years invited ridicule…(More)”.

The Rise of Silicon Valley’s Techno-Religion

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