Paper by Geoff Mulgan: “I summarise the perspectives of different disciplines – economics, psychology, computer science, business studies, organisation studies, political science, history, law, international relations, anthropology, design and complexity. In each case I make short suggestions on what would be useful from each, before turning to what a more synthetic approach might look like, in particular using insights from biology and computation to see organisations as living things and addressing the dynamics of ecosystems of organisations which compete and cooperate.

The paper asks of the people working in academic disciplines: how are you engaging with, and learning from, other disciplines? And how could your knowledge be useful to a world that badly needs to reform its public institutions at every level, from the local to the global?…(More)”.

Blog by Stefaan Verhulst and Adam Zable: “The world’s relationship to data is changing rapidly. Artificial intelligence has generated significant excitement for its potential to help solve public problems, improve decision-making, and create new forms of economic and social value. At the same time, it has intensified longstanding debates around access, ownership, attribution, privacy, labor rights, security, and the responsible reuse of public-interest data. Questions that once sat at the margins of data policy have moved to the center.

Governments, international organizations, civil society groups, and companies are responding with a growing range of governance approaches, from executive orders and regulatory frameworks to international agreements, industry standards, and new institutional models. Yet it remains difficult to distinguish short-term developments from deeper structural shifts. While discussion often focuses on the latest AI breakthrough, the more important question for policymakers is how these technologies are reshaping the systems, assumptions, and governance models that determine how data is accessed, shared, and used.

To better understand these changes, The GovLab convened two forecasting studios bringing together experts working in data governance, digital policy, open science, AI governance, and public-sector innovation. Participants explored emerging trends in data access, governance, and reuse, examined the forces driving those trends, and considered what they may mean for the future of open data and public-interest data ecosystems.

The discussions identified seven signals that point to significant changes already underway. These signals suggest that the future of data governance will be shaped by advances in AI alongside evolving expectations around trust, stewardship, infrastructure, sovereignty, reciprocity, and public value. They offer a starting point for understanding how data policy may need to evolve in the years ahead. A longer version of this analysis, with a full list of studio participants and more detailed discussion of each signal, will be published separately…(More)”.

Book by Jibu Elias: “Artificial intelligence has been affecting the way people think, work and create, and the questions that have arisen in its wake are as pressing as they are uncomfortable. Who benefits from this new technology? Who is left behind? And what happens when the tools we build begin to govern us?
In an age where humans are dazzled by machines that seem to think, Jibu Elias -researcher, writer and advisor on AI governance-peels back the glossy surface to reveal systems driven more by prediction than true intelligence; and a world where algorithms redesign economies, redraw social boundaries and challenge the very idea of human agency. Drawing on nearly a decade of experience across the Indian and the global AI governance landscape, Elias points squarely at the widening gaps between promises and reality: from mass job displacements and deepening biases embedded in AI systems to the rapid consolidation of power by tech giants shaping our future and the heavy environmental costs of unregulated innovation.
Moving beyond Silicon Valley optimism, The New Divide is a vital perspective from the Global South. There is a very small window of action open to us in the face of the rapidly accelerating use of AI. Ethical governance and regulation are imperative, and we need guidelines now. This book is a call to action. As AI reshapes what it means to be human, we must reclaim control. Before it’s too late…(More)”.

Article by Maximilian Henning: “…The tool Blanchett launched – called the “Human Consent Registry” – allows people to signal whether they give permission for AI companies to use their likeness, or whether they would prefer to be asked or paid first.

These preferences are then put into a machine-readable form that AI can, in theory, easily read. But crucially, the registry is meant to be voluntary, which means AI companies would have to agree to adhere to people’s preferences.

Blanchett acknowledged this key limitation on Tuesday.

“A registry will not solve all the problems overnight. But every standard starts somewhere,” she said.

She also pointed out that while the registry is voluntary for now, it could become “part of the practical infrastructure” to assist binding laws and rules later on. It could, for example, give regulators evidence to enforce consequences if consent is not respected, she said.

The right to your face

Under the EU’s AI Act, AI companies must respect a person’s request not to use their creative work for AI training. The Commission is currently leading talks between tech giants and copyright holders over adding technical mechanisms for this, though these talks are making little progress.

Still, Blanchett’s registry will soon also allow people to say whether AI can use their work, and it could conceivably become one such mechanism.

The situation is more complicated regarding people’s identities, which the registry also aims to protect. Denmark is moving towards giving its citizens a say over AI deepfakes, with Cyprus following suit. However, so far, these are national initiatives, not EU-wide.

And the issue is legally complicated, touching on existing rules on privacy or platform regulation, while copyright rules aim to protect artistic works rather than faces or voices.

EU aware of a problem

The European Commission has recently announced it is working on a law governing how people can licence their creative work to AI companies – and acknowledged that artists face tricky issues in this field.

“Performers face certain challenges in relation to AI-generated imitations of their personal characteristics and performances (‘impersonifications’), which raise complex questions going beyond copyright protection,” said a public consultation on the new legal initiative…(More)”.

Paper by Elettra Bietti: “The ability to direct and receive attention is constitutive of human life. Humans have an inborn need for attention, and an inborn ability to direct attention for survival. Yet attention is not just a creature of an individual’s mind. It is a relationship between people and their environment. As such, our attention is shaped by the material, social and economic conditions that surround us. Today, people’s attention is increasingly extracted and colonized through technology. Attention platforms and AI technologies are transforming the shape, objects, metrics and value of human time and attention.

This article focuses on the role of data-attention platforms in transforming time and attention. Data-attention platforms include social media platforms such as Facebook, YouTube, TikTok, and increasingly AI companions such as Replika or Character.AI. They capture data and attention and draw revenues from them, primarily but not exclusively through surveillance advertising. The business models of data-attention platforms are organized around the data-attention imperative, the drive to continuously capture troves of data and attention to generate value. They capture eyeballs to sell ads and collect data to target ads and maximize engagement. Time online enables more data collection, which, in turn allows for the design of products that more effectively addict users. This extractive data-attention spiral produces a harmful commodification and erosion of time and attention which shrinks the human experience and undermines collective life.

This article asks how governments should and shouldn’t regulate data-attention platform business models and the distortions they cause. It is tempting to reduce growing data-attention disorders to problems of individual choice online, delegating solutions to market-based tools, more competition or the exercise of individual data protection rights and parental controls. Instead, the answer requires moving past individual preferences and embracing an infrastructural approach focused on changing platform incentives and technological affordances and on safeguarding space for offline time. Privacy and data protection, child social media regulations and productivity tools provide for controls and safeguards that too often magnify instead of addressing attention disorders. The idea of individual autonomy that underlies them is unfit for the attention era. The article advocates a conception that takes the power of platforms to shape our attention seriously and advocates for the protection of children and adults’ time away from technology. Time away from technology is a collective good in need of protection. Based on a three-fold agenda that incorporates design changes, taxation, and legal reform to reduce time spent online as well as the speed and scale of the digital experience, the article aims to bring attention platform ecosystems in greater alignment with the interests of society without placing unrealistic expectations on individual users and parents…(More)”.

Paper by Daniel Berliner: “Participatory institutions often aim to yield information useful to policymakers, whether about public preferences, problems, or solutions. But how can large numbers of public contributions be processed into interpretable and actionable information outputs? As theorists and practitioners increasingly call for participatory institutions to operate at larger scales, often enabled by new technologies, this challenge only becomes more important. This article reviews recent work on participatory institutions in order to develop several insights: (a) that there are different types of information that policymakers may aim to learn and that are relevant to different policy stages; (b) that information must be effectively processed in order to be interpretable and actionable for policymakers; (c) that there are different types of information processing, depending on the specificity and novelty of the information outputs that policymakers aim to learn; and (d) that there are different ways in which this processing can be delegated, whether to experts, ordinary people, or automated algorithms. Better recognizing these differences will help both researchers and practitioners better understand the potential and the limitations of participatory institutions in different settings and with different goals…(More)”.

Handbook by Cathy Riley et al: “…provides an in-depth guide to planning and sustaining a Mobile Phone Data (MPD) initiative, with a primary focus on the use of Call Detail Records (CDRs) for public policy, statistical, and development purposes, including operational decision-making. It builds on, and develops further, the concepts and principles first described in the original Handbook on the Use of Mobile Phone Data for Official Statistics released by what was then known as the UN Global Working Group on Big Data for Official Statistics. (United Nations Statistics Division 2019)

The handbook is intended for practitioners working in national statistical offices, telecom regulators, mobile network operators, government ministries, and partner organisations who would like to initiate an MPD initiative. It also contains advice and guidance for those who may already have embarked on the journey of establishing such an initiative but who are searching for more information or guidance on how to do so effectively and sustainably. It is designed to enable such readers to understand not only the steps involved in planning an MPD initiative, but also the technical, institutional, legal, and ethical reasoning that underpins each decision. It is suitable for both technical and non-technical audiences, and does not assume deep prior technical expertise in MPD analytics…(More)”.

Report by DARE UK (Data and Analytics Research Environments UK): “…offering a detailed, UK-wide picture of how Trusted Research Environments (TREs) are supporting research for public benefit.

Building on early insights shared late last year, the full report brings together findings from a 2025 survey of 63 organisations across universities, government, charities and the private sector. It provides one of the most comprehensive overviews to date of how TREs operate, how they are funded and how they are evolving to meet growing demand.

Enabling research while protecting privacy 

The review highlights the central role of TREs in the UK’s approach to using sensitive data responsibly. These highly secure computing environments allow approved researchers to analyse sensitive datasets without the data leaving a controlled setting.

TREs make it possible to carry out vital research using data from areas such as health, education and social care, while maintaining strict safeguards and public trust.

DARE UK’s work focuses on strengthening and connecting these environments to support trustworthy, consistent and high-quality sensitive data research in the UK.

A growing and increasingly capable ecosystem

The review confirms that the UK has a large and expanding TRE ecosystem. The organisations surveyed together support nearly 7,000 active research projects per year using sensitive data, demonstrating the scale and importance of this infrastructure.

Most activity sits within universities and the public sector, with TREs operating across all four UK nations, although capacity and capability vary between regions.

The review also shows that many organisations perform multiple roles across the system, reflecting the collaborative and interconnected nature of sensitive data research…(More)”.

Press Release: “As artificial intelligence increasingly relies on language and cultural data, Indigenous communities face unprecedented opportunities and significant risks. While Indigenous languages and cultural knowledge can help shape more inclusive digital futures, too often communities have limited influence over how their data is collected, governed, used, or shared.

To address this challenge, The GovLab, Microsoft, and UNESCO are launching the New Commons Incubator for Indigenous Languages and Culture, a new initiative designed to support Indigenous-led efforts to develop data commons that preserve, steward, and responsibly govern language and cultural resources in the AI era.

Data commons are shared governance frameworks that enable communities to collectively decide how their data is managed, accessed, and used while ensuring that benefits flow back to the communities themselves. By supporting the development of Indigenous-led data commons, the Incubator seeks to strengthen community agency, support language revitalization, and ensure that Indigenous peoples can participate in shaping the future of AI on their own terms.

The Incubator is a capacity-building initiative that provides mentorship, training, technical guidance, and proposal development support. Participants will receive an in-person opportunity to collaborate and network with other participants, followed by six months of workshops, one-on-one clinics, expert mentorships, and peer learning opportunities. The program is designed to help teams prepare stronger proposals and will end with a final showcase where participants present these to potential funders, partners, and collaborators…(More)”.

Book by David Hand: “Statistics and data science aim to extract understanding from data and guide decision-making. However, before applying any analytical tools, we need absolute clarity about what we want to know or accomplish. Ambiguous objectives inevitably lead to mistaken conclusions and flawed actions. This book investigates the deeper challenges of formulating clear questions and matching analytical methods to those questions – issues that apply as much to elementary statistical tools as to sophisticated techniques. Rather than focusing on standard statistical misuses or data provenance issues, this work examines the critical step of ensuring your analysis actually answers the question you mean to ask.

Drawing from collaborative work across finance, medicine, government, manufacturing, defence, and other fields, the book deliberately emphasises basic and familiar tools so the fundamental issues are accessible to everyone. Following John Tukey’s insight about the simplest problems of data analysis, the most detailed discussions centre on averages and comparisons between distributions, though the principles apply with even greater force to advanced methods that fewer people fully understand.

Key Features: 

• Focusses on question formulation rather than computational techniques, addressing the step that precedes all successful data analysis

• Emphasises basic statistical tools (averages, comparisons) to make fundamental challenges visible to all practitioners

• Contains 130 text boxes presenting essential ideas in non-technical language, creating a “two-in-one” book accessible to both mathematical and non-mathematical readers

• Provides real-world examples drawn from diverse fields including finance, healthcare, government, manufacturing, and defence

• Offers a deep-dive analysis of a specific comparison method to illustrate the care required for precise statistical reasoning

• Presents a progression from general principles through detailed mathematical exploration to practical applications across various analytical scenarios

This book serves as an essential guide for statisticians, data scientists, researchers, and anyone who uses data to make decisions. Whether you’re a practitioner seeking to improve your analytical approach or a student learning to think critically about statistical questions, this work will help you use data analytical tools more effectively and avoid the costly mistakes that arise from asking the wrong questions of your data…(More)”.