Article by Solveig Bjørkholt: “This article presents an original database on international standards, constructed using modern data gathering methods. StanDat facilitates studies into the role of standards in the global political economy by (1) being a source for descriptive statistics, (2) enabling researchers to assess scope conditions of previous findings, and (3) providing data for new analyses, for example the exploration of the relationship between standardization and trade, as demonstrated in this article. The creation of StanDat aims to stimulate further research into the domain of standards. Moreover, by exemplifying data collection and dissemination techniques applicable to investigating less-explored subjects in the social sciences, it serves as a model for gathering, systematizing, and sharing data in areas where information is plentiful yet not readily accessible for research…(More)”.
Disinformation: Definitions and examples
Explainer by Perthusasia Centre: “Disinformation has been a tool of manipulation and control for centuries, from ancient military strategies to Cold War propaganda. With the rapid advancement of technology,
it has evolved into a sophisticated and pervasive security threat that transcends traditional boundaries.
This explainer takes the definitions and examples from our recent Indo-Pacific Analysis Brief, Disinformation and cognitive warfare by Senior Fellow Alana Ford, and creates an simple, standalone guide for quick reference…(More)”.
Diversifying Professional Roles in Data Science
Policy Briefing by Emma Karoune and Malvika Sharan: The interdisciplinary nature of the data science workforce extends beyond the traditional notion of a “data scientist.” A successful data science team requires a wide range of technical expertise, domain knowledge and leadership capabilities. To strengthen such a team-based approach, this note recommends that institutions, funders and policymakers invest in developing and professionalising diverse roles, fostering a resilient data science ecosystem for the future.
By recognising the diverse specialist roles that collaborate within interdisciplinary teams, organisations can leverage deep expertise across multiple skill sets, enhancing responsible decision-making and fostering innovation at all levels. Ultimately, this note seeks to shift the perception of data science professionals from the conventional view of individual data scientists to a competency-based model of specialist roles within a team, each essential to the success of data science initiatives…(More)”.
To Stop Tariffs, Trump Demands Opioid Data That Doesn’t Yet Exist
Article by Josh Katz and Margot Sanger-Katz: “One month ago, President Trump agreed to delay tariffs on Canada and Mexico after the two countries agreed to help stem the flow of fentanyl into the United States. On Tuesday, the Trump administration imposed the tariffs anyway, saying that the countries had failed to do enough — and claiming that tariffs would be lifted only when drug deaths fall.
But the administration has seemingly established an impossible standard. Real-time, national data on fentanyl overdose deaths does not exist, so there is no way to know whether Canada and Mexico were able to “adequately address the situation” since February, as the White House demanded.
“We need to see material reduction in autopsied deaths from opioids,” said Howard Lutnick, the commerce secretary, in an interview on CNBC on Tuesday, indicating that such a decline would be a precondition to lowering tariffs. “But you’ve seen it — it has not been a statistically relevant reduction of deaths in America.”
In a way, Mr. Lutnick is correct that there is no evidence that overdose deaths have fallen in the last month — since there is no such national data yet. His stated goal to measure deaths again in early April will face similar challenges.
But data through September shows that fentanyl deaths had already been falling at a statistically significant rate for months, causing overall drug deaths to drop at a pace unlike any seen in more than 50 years of recorded drug overdose mortality data.
The declines can be seen in provisional data from the Centers for Disease Control and Prevention, which compiles death records from states, which in turn collect data from medical examiners and coroners in cities and towns. Final national data generally takes more than a year to produce. But, as the drug overdose crisis has become a major public health emergency in recent years, the C.D.C. has been publishing monthly data, with some holes, at around a four-month lag…(More)”.
GeoTechnoGraphy: Mapping Power and Identity in the Digital Age
Book by Samir Saran and Anirban Sarma: “In an era defined by rapid technological change, a seismic shift is underway. From the rise of digital platforms that mediate our interactions—with markets, with governments and perhaps most importantly with each other as citizens— to the growing tension between our online personas and our real-world identities, the forces of technology, geography and society are colliding in ways we are only beginning to understand.
Even as technology opens up new opportunities for civic engagement, it simultaneously disrupts the very foundations of societal cohesion. The digital age has given rise to a new stage for global drama—one where surveillance, the weaponization of information and the erosion of trust in national and multilateral institutions are playing out in real time. But as these forces evolve, so too must our understanding of how individuals and societies can navigate them.
Will digital societies endure, or are they doomed to collapse under the weight of their own contradictions? Can democracy as we know it survive in a world where power is increasingly concentrated in the hands of a few tech giants? And as nations grapple with the changing dynamics of governance, how will international norms, laws and institutions adapt?
In GeoTechnoGraphy, Samir Saran and Anirban Sarma offer a compelling analysis of the forces reshaping the modern world. Drawing on groundbreaking research and incisive insights, they examine how the convergence of geography and technology—geotechnography—is redefining power and writing new rules for its exercise…(More)”
Commerce Secretary’s Comments Raise Fears of Interference in Federal Data
Article by Ben Casselman and Colby Smith: “Comments from a member of President Trump’s cabinet over the weekend have renewed concerns that the new administration could seek to interfere with federal statistics — especially if they start to show that the economy is slipping into a recession.
In an interview on Fox News on Sunday, Howard Lutnick, the commerce secretary, suggested that he planned to change the way the government reports data on gross domestic product in order to remove the impact of government spending.
“You know that governments historically have messed with G.D.P.,” he said. “They count government spending as part of G.D.P. So I’m going to separate those two and make it transparent.”
It wasn’t immediately clear what Mr. Lutnick meant. The basic definition of gross domestic product is widely accepted internationally and has been unchanged for decades. It tallies consumer spending, private-sector investment, net exports, and government investment and spending to arrive at a broad measure of all goods and services produced in a country.The Bureau of Economic Analysis, which is part of Mr. Lutnick’s department, already produces a detailed breakdown of G.D.P. into its component parts. Many economists focus on a measure — known as “final sales to private domestic purchasers” — that excludes government spending and is often seen as a better indicator of underlying demand in the economy. That measure has generally shown stronger growth in recent quarters than overall G.D.P. figures.
In recent weeks, however, there have been mounting signs elsewhere that the economy could be losing momentum. Consumer spending fell unexpectedly in January, applications for unemployment insurance have been creeping upward, and measures of housing construction and home sales have turned down. A forecasting model from the Federal Reserve Bank of Atlanta predicts that G.D.P. could contract sharply in the first quarter of the year, although most private forecasters still expect modest growth.
Cuts to federal spending and the federal work force could act as a further drag on economic growth in coming months. Removing federal spending from G.D.P. calculations, therefore, could obscure the impact of the administration’s policies…(More)”.
Citizen participation and technology: lessons from the fields of deliberative democracy and science and technology studies
Paper by Julian “Iñaki” Goñi: “Calls for democratising technology are pervasive in current technological discourse. Indeed, participating publics have been mobilised as a core normative aspiration in Science and Technology Studies (STS), driven by a critical examination of “expertise”. In a sense, democratic deliberation became the answer to the question of responsible technological governance, and science and technology communication. On the other hand, calls for technifying democracy are ever more pervasive in deliberative democracy’s discourse. Many new digital tools (“civic technologies”) are shaping democratic practice while navigating a complex political economy. Moreover, Natural Language Processing and AI are providing novel alternatives for systematising large-scale participation, automated moderation and setting up participation. In a sense, emerging digital technologies became the answer to the question of how to augment collective intelligence and reconnect deliberation to mass politics. In this paper, I explore the mutual shaping of (deliberative) democracy and technology (studies), highlighting that without careful consideration, both disciplines risk being reduced to superficial symbols in discourses inclined towards quick solutionism. This analysis highlights the current disconnect between Deliberative Democracy and STS, exploring the potential benefits of fostering closer links between the two fields. Drawing on STS insights, the paper argues that deliberative democracy could be enriched by a deeper engagement with the material aspects of democratic processes, the evolving nature of civic technologies through use, and a more critical approach to expertise. It also suggests that STS scholars would benefit from engaging more closely with democratic theory, which could enhance their analysis of public participation, bridge the gap between descriptive richness and normative relevance, and offer a more nuanced understanding of the inner functioning of political systems and politics in contemporary democracies…(More)”.
Future of AI Research
Report by the Association for the Advancement of Artificial Intelligence: “As AI capabilities evolve rapidly, AI research is also undergoing a fast and significant transformation along many dimensions, including its topics, its methods, the research community, and the working environment. Topics such as AI reasoning and agentic AI have been studied for decades but now have an expanded scope in light of current AI capabilities and limitations. AI ethics and safety, AI for social good, and sustainable AI have become central themes in all major AI conferences. Moreover, research on AI algorithms and software systems is becoming increasingly tied to substantial amounts of dedicated AI hardware, notably GPUs, which leads to AI architecture co-creation, in a way that is more prominent now than over the last 3 decades. Related to this shift, more and more AI researchers work in corporate environments, where the necessary hardware and other resources are more easily available, compared to academia, questioning the roles of academic AI research, student retention, and faculty recruiting. The pervasive use of AI in our daily lives and its impact on people, society, and the environment makes AI a socio-technical field of study, thus highlighting the need for AI researchers to work with experts from other disciplines, such as psychologists, sociologists, philosophers, and economists. The growing focus on emergent AI behaviors rather than on designed and validated properties of AI systems renders principled empirical evaluation more important than ever. Hence the need arises for well-designed benchmarks, test methodologies, and sound processes to infer conclusions from the results of computational experiments. The exponentially increasing quantity of AI research publications and the speed of AI innovation are testing the resilience of the peer-review system, with the immediate release of papers without peer-review evaluation having become widely accepted across many areas of AI research. Legacy and social media increasingly cover AI research advancements, often with contradictory statements that confuse the readers and blur the line between reality and perception of AI capabilities. All this is happening in a geo-political environment, in which companies and countries compete fiercely and globally to lead the AI race. This rivalry may impact access to research results and infrastructure as well as global governance efforts, underscoring the need for international cooperation in AI research and innovation.
In this overwhelming multi-dimensional and very dynamic scenario, it is important to be able to clearly identify the trajectory of AI research in a structured way. Such an effort can define the current trends and the research challenges still ahead of us to make AI more capable and reliable, so we can safely use it in mundane but also, most importantly, in high-stake scenarios.
This study aims to do this by including 17 topics related to AI research, covering most of the transformations mentioned above. Each chapter of the study is devoted to one of these topics, sketching its history, current trends and open challenges…(More)”.
Legitimacy: Working hypotheses
Report by TIAL: “Today more than ever, legitimacy is a vital resource for institutions seeking to lead and sustain impactful change. Yet, it can be elusive.
What does it truly mean for an institution to be legitimate? This publication delves into legitimacy as both a practical asset and a dynamic process, offering institutional entrepreneurs the tools to understand, build, and sustain it over time.
Legitimacy is not a static quality, nor is it purely theoretical. Instead, it’s grounded in the beliefs of those who interact with or are governed by an institution. These beliefs shape whether people view an institution’s authority as rightful and worth supporting. Drawing from social science research and real-world insights, this publication provides a framework to help institutional entrepreneurs address one of the most important challenges of institutional design: ensuring their legitimacy is sufficient to achieve their goals.
The paper emphasizes that legitimacy is relational and contextual. Institutions gain it through three primary sources: outcomes (delivering results), fairness (ensuring just processes), and correct procedures (following accepted norms). However, the need for legitimacy varies depending on the institution’s size, scope, and mission. For example, a body requiring elite approval may need less legitimacy than one relying on mass public trust.
Legitimacy is also dynamic—it ebbs and flows in response to external factors like competition, crises, and shifting societal narratives. Institutional entrepreneurs must anticipate these changes and actively manage their strategies for maintaining legitimacy. This publication highlights actionable steps for doing so, from framing mandates strategically to fostering public trust through transparency and communication.
By treating legitimacy as a resource that evolves over time, institutional entrepreneurs can ensure their institutions remain relevant, trusted, and effective in addressing pressing societal challenges.
Key takeaways
- Legitimacy is the belief by an audience that an institution’s authority is rightful.
- Institutions build legitimacy through outcomes, fairness, and correct procedures.
- The need for legitimacy depends on an institution’s scope and mission.
- Legitimacy is dynamic and shaped by external factors like crises and competition.
- A portfolio approach to legitimacy—balancing outcomes, fairness, and procedure—is more resilient.
- Institutional entrepreneurs must actively manage perceptions and adapt to changing contexts.
- This publication offers practical frameworks to help institutional entrepreneurs build and sustain legitimacy…(More)”.
AI could supercharge human collective intelligence in everything from disaster relief to medical research
Article by Hao Cui and Taha Yasseri: “Imagine a large city recovering from a devastating hurricane. Roads are flooded, the power is down, and local authorities are overwhelmed. Emergency responders are doing their best, but the chaos is massive.
AI-controlled drones survey the damage from above, while intelligent systems process satellite images and data from sensors on the ground and air to identify which neighbourhoods are most vulnerable.
Meanwhile, AI-equipped robots are deployed to deliver food, water and medical supplies into areas that human responders can’t reach. Emergency teams, guided and coordinated by AI and the insights it produces, are able to prioritise their efforts, sending rescue squads where they’re needed most.
This is no longer the realm of science fiction. In a recent paper published in the journal Patterns, we argue that it’s an emerging and inevitable reality.
Collective intelligence is the shared intelligence of a group or groups of people working together. Different groups of people with diverse skills, such as firefighters and drone operators, for instance, work together to generate better ideas and solutions. AI can enhance this human collective intelligence, and transform how we approach large-scale crises. It’s a form of what’s called hybrid collective intelligence.
Instead of simply relying on human intuition or traditional tools, experts can use AI to process vast amounts of data, identify patterns and make predictions. By enhancing human decision-making, AI systems offer faster and more accurate insights – whether in medical research, disaster response, or environmental protection.
AI can do this, by for example, processing large datasets and uncovering insights that would take much longer for humans to identify. AI can also get involved in physical tasks. In manufacturing, AI-powered robots can automate assembly lines, helping improve efficiency and reduce downtime.
Equally crucial is information exchange, where AI enhances the flow of information, helping human teams coordinate more effectively and make data-driven decisions faster. Finally, AI can act as social catalysts to facilitate more effective collaboration within human teams or even help build hybrid teams of humans and machines working alongside one another…(More)”.