Stefaan Verhulst
Paper by Arianna Zuanazzi, Michael P. Milham & Gregory Kiar: “Modern brain science is inherently multidisciplinary, requiring the integration of neuroimaging, psychology, behavioral science, genetics, computational neuroscience and artificial intelligence (to name a few) to advance our understanding of the brain. Critical challenges in the field of brain health — including clinical psychology, cognitive and brain sciences, and digital mental health — include the great heterogeneity of human data, small sample sizes and the subjectivity or limited reproducibility of measured constructs. Large-scale, multi-site and multimodal open science initiatives can represent a solution to these challenges (for example, see refs.); however, they often struggle with balancing data quality while maximizing sample size5 and ensuring that the resulting data are findable, accessible, interoperable and reusable (FAIR). Furthermore, large-scale high-dimensional multimodal datasets demand advanced analytic approaches beyond conventional statistical models, requiring the expertise and interdisciplinary collaboration of the broader scientific community…
Data science competitions (such as Kaggle, DrivenData, CodaBench and AIcrowd) offer a powerful mechanism to bridge disciplines, solve complex problems and crowdsource novel solutions, as they bring individuals from around the world together to solve real-world problems. For more than 20 years (for example, see refs.), such competitions have been hosted by companies, organizations and research institutions to answer scientific questions, advance methods and techniques, extract valuable insights from data, promote organizations’ missions and foster collaboration with stakeholders. Every stage of a data science competition offers opportunities to promote big data exploration, advance analytic innovation and strengthen community engagement (Fig. 1). To translate these opportunities into actionable steps, we have shared our Data Science Competition Organizer Checklist at https://doi.org/10.17605/osf.io/hnx9b; this offers practical guidance for designing and implementing data science competitions in the brain health domain…(More)”
Paper by the Knight-Georgetown Institute (KGI): “Online platforms and services shape what we know, how we connect, and who gets heard. From elections and public health to commerce and conflict, platforms are now indispensable infrastructure for civic life. Their influence is vast, and so is the need to understand them.
As critical conversations publicly unfold on digital platforms, the ability to study these posts and content at scale has steadily diminished. Tools like Facebook’s CrowdTangle – which once offered researchers, journalists, and civil society a window into public online discourse – have disappeared. Meta, Reddit, and X have restricted data access tools that were once widely available, and researchers have faced threats of litigation for accessing public platform data.
Platforms restrict researcher access while public data is increasingly monetized for advertisers, data brokers, and training artificial intelligence (AI) systems. This imbalance – where companies profit while independent researchers are left in the dark – undermines transparency, limits free expression, and weakens oversight.
That is the reason for developing Better Access, a baseline framework for independent access to public platform data: the content, data, and information posted to platforms that anyone can access. …(More)”.
Article by Thomas R. Karl, Stephen C. Diggs, Franklin Nutter, Kevin Reed, and Terence Thompson: “From farming and engineering to emergency management and insurance, many industries critical to daily life rely on Earth system and related socioeconomic datasets. NOAA has linked its data, information, and services to trillions of dollars in economic activity each year, and roughly three quarters of U.S. Fortune 100 companies use NASA Earth data, according to the space agency.
Such data are collected in droves every day by an array of satellites, aircraft, and surface and subsurface instruments. But for many applications, not just any data will do.
Leaving reference quality datasets (RQDs) to languish, or losing them altogether, would represent a dramatic shift in the country’s approach to managing environmental risk.
Trusted, long-standing datasets known as reference quality datasets (RQDs) form the foundation of hazard prediction and planning and are used in designing safety standards, planning agricultural operations, and performing insurance and financial risk assessments, among many other applications. They are also used to validate weather and climate models, calibrate data from other observations that are of less than reference quality, and ground-truth hazard projections. Without RQDs, risk assessments grow more uncertain, emergency planning and design standards can falter, and potential harm to people, property, and economies becomes harder to avoid.
Yet some well-established, federally supported RQDs in the United States are now slated to be, or already have been, decommissioned, or they are no longer being updated or maintained because of cuts to funding and expert staff. Leaving these datasets to languish, or losing them altogether, would represent a dramatic—and potentially very costly—shift in the country’s approach to managing environmental risk…(More)”.
Paper by Cheng-Chun Lee et al: “Using novel data and artificial intelligence (AI) technologies in crisis resilience and management is increasingly prominent. AI technologies have broad applications, from detecting damages to prioritizing assistance, and have increasingly supported human decision-making. Understanding how AI amplifies or diminishes specific values and how responsible AI practices and governance can mitigate harmful outcomes and protect vulnerable populations is critical. This study presents a responsible AI roadmap embedded in the Crisis Information Management Circle. Through three focus groups with participants from diverse organizations and sectors and a literature review, we develop six propositions addressing important challenges and considerations in crisis resilience and management. Our roadmap covers a broad spectrum of interwoven challenges and considerations on collecting, analyzing, sharing, and using information. We discuss principles including equity, fairness, explainability, transparency, accountability, privacy, security, inter-organizational coordination, and public engagement. Through examining issues around AI systems for crisis management, we dissect the inherent complexities of information management, governance, and decision-making in crises and highlight the urgency of responsible AI research and practice. The ideas presented in this paper are among the first attempts to establish a roadmap for actors, including researchers, governments, and practitioners, to address important considerations for responsible AI in crisis resilience and management…(More)”.
Article by Dilek Fraisl et al: “The termination in February 2025 of the Demographic and Health Surveys, a critical source of data on population, health, HIV, and nutrition in over 90 countries, supported by the United States Agency for International Development, constitutes a crisis for official statistics. This is particularly true for low- and middle-income countries that lack their own survey infrastructure1. At a national level, in the United States, proposed cuts to the Environmental Protection Agency by the current administration further threaten the capacity to monitor and achieve environmental sustainability and implement the SDGs2,3. Citizen science—data collected through voluntary public contributions—now can and must step up to fill the gap and play a more central role in official statistics.
Demographic and Health Surveys contribute directly to the calculation of around 30 of the indicators that underpin the Sustainable Development Goals (SDGs)4. More generally, a third of SDG indicators rely on household surveys data5.
Recent political changes, particularly in the United States, have exposed the risks of relying too heavily on a single country or institution to run global surveys and placing minimal responsibility on individual countries for their own data collection.
Many high-income countries, particularly European ones, are experiencing similar challenges and financial pressures on their statistical systems as their national budgets are increasingly prioritizing defense spending6. Along with these budget cuts comes a risk that perceived efficiency gains from artificial intelligence are increasingly viewed as a pretense to put further budgetary pressure on official statistical agencies7.
In this evolving environment, we argue that citizen science can become an essential part of national data gathering efforts. To date, policymakers, researchers, and agencies have viewed it as supplementary to official statistics. Although self-selected participation can introduce bias, citizen science provides fine-scale, timely, cost-efficient, and flexible data that can fill gaps and help validate official statistics. We contend that, rather than an optional complement, citizen science data should be systematically integrated into national and global data ecosystems…(More)”.
Working Paper by Geoff Mulgan and Caio Werneck: “City governments across the world usually organise much of their work through functional hierarchies – departments or secretariats with specialised responsibility for transport, housing, sanitation, education, environment and so on. Their approaches mirror those of national governments and the traditional multi-divisional business which had separate teams for manufacturing, marketing, sales, and for different product lines.
Those hierarchical structures became the norm in the late 19th century and they still work well for stable, bounded problems. They ensure clear accountability; a concentration of specialised knowledge; and a means to engage relevant stakeholders. Often, they bring together officials and professionals with a strong shared ethos – whether for policing or education, transport or housing.
But vertical silos have also always created problems. Many priorities don’t fit them neatly. Sometimes departments clash, or dump costs onto each other. They may fail to share vital information.
There is a long history of attempts to create more coherent, coordinated ways of working, and as cities face overlapping emergencies (from pandemics to climate disasters), and slow-burning crises (in jobs, care, security and housing) that cut across these silos, many are looking for new ways to coordinate action.
Some of the new options make the most of digital technologies which make it much easier to organise horizontally – with shared platforms, data or knowledge, or one-stop shops or portals for citizens. Some involve new roles (for digital, heat or resilience), new types of team or task force (such as I-Teams for innovation). And many involve new kinds of partnership or collaboration, with mesh-like structures instead of the traditional pyramid hierarchies of public administration…(More)”
Paper by Edith Darin: “The digital era has transformed the production and governance of demographic figures, shifting it from a collective, state-led endeavour to one increasingly shaped by private actors and extractive technologies. This paper analyses the implications of these shifts by tracing the evolving status of demographic figures through the lens of Ostrom’s typology of goods: from a club good in royal censuses, to a public good under democratic governance, and now towards a private asset whose collection has become rivalrous and its dissemination excludable. Drawing on case studies involving satellite imagery, mobile phone data, and social media platforms, the study shows how new forms of passive data collection while providing previously unseen data opportunities, disrupt also traditional relationships between states and citizens, raise ethical and epistemic concerns, and challenge the legitimacy of national statistical institutes. In response, the paper advocates for the reconstitution of demographic figures as a common good, proposing a collective governance model that includes increased transparency, the sharing of anonymised aggregates, and the creation of a Public Demographic Data Library to support democratic accountability and technical robustness in demographic knowledge production…(More)”.
Report by OpenAI: “More than 5% of all ChatGPT messages globally are about healthcare, averaging billions of messages each week. Of our more than 800 million regular users, one in four submits a prompt about healthcare every week. More than 40 million turn to ChatGPT every day with healthcare questions.
In the United States, the healthcare system is a long-standing and worsening pain point for many. Gallup finds that views of US healthcare quality have sunk to a 24-year-low; that Americans give the system a C+ on access and a D+ on costs; and that a combined 70% believe the system has major problems or is in a state of crisis. In our own research, three in five Americans say the current system is broken, and strong majorities tell us that hospital costs (87%), poor healthcare access (77%), and a lack of nurses (75%) are all serious problems.
For both patients and providers in the US, ChatGPT has become an important ally, helping people navigate the healthcare system, enabling them to self-advocate, and supporting both patients and providers for better health outcomes.
Based on anonymized ChatGPT message data:
– Nearly 2 million messages per week focus on health insurance, including for comparing plans, understanding prices, handling claims and billing, eligibility and enrollment, and coverage and cost-sharing details.
– In underserved rural communities, users send an average of nearly 600,000 healthcare-related messages every week.
– And seven in 10 healthcare conversations in ChatGPT happen outside of normal clinic hours.
This report details: (1) how users are turning to ChatGPT for help in navigating the US healthcare system; (2) how they’re turning to ChatGPT to help them close healthcare access gaps, including in “hospital deserts” across the country; and (3) how healthcare providers and workers are using AI in their roles now…(More)”.
Paper by Hangcheng Zhao and Ron Berman: “Large language models (LLMs) change how consumers acquire information online; their bots also crawl news publishers’ websites for training data and to answer consumer queries; and they provide tools that can lower the cost of content creation. These changes lead to predictions of adverse impact on news publishers in the form of lowered consumer demand, reduced demand for newsroom employees, and an increase in news “slop.” Consequently, some publishers strategically responded by blocking LLM access to their websites using the robots.txt
file standard.
Using high-frequency granular data, we document four effects related to the predicted shifts in news publishing following the introduction of generative AI (GenAI). First, we find a consistent and moderate decline in traffic to news publishers occurring after August 2024. Second, using a difference-in-differences approach, we find that blocking GenAI bots can have adverse effects on large publishers by reducing total website traffic by 23% and real consumer traffic by 14% compared to not blocking. Third, on the hiring side, we do not find evidence that LLMs are replacing editorial or content-production jobs yet. The share of new editorial and contentproduction job listings increases over time. Fourth, regarding content production, we find no evidence that large publishers increased text volume; instead, they significantly increased rich content and use more advertising and targeting technologies.
Together, these findings provide early evidence of some unforeseen impacts of the introduction of LLMs on news production and consumption…(More)”.
Whitepaper by Frontiers: “…shows that AI has rapidly become part of everyday peer review, with 53% of reviewers now using AI tools. The findings in Unlocking AI’s untapped potential: responsible innovation in research and publishing point to a pivotal moment for research publishing. Adoption is accelerating and the opportunity now is to translate this momentum into stronger, more transparent, and more equitable research practices as demonstrated in Frontiers’ policy outlines.
Drawing on insights from 1,645 active researchers worldwide, the whitepaper identifies a global community eager to use AI confidently and responsibly. While many reviewers currently rely on AI for drafting reports or summarizing findings, the report highlights significant untapped potential for AI to support rigor, reproducibility, and deeper methodological insight.
The study shows broad enthusiasm for using AI more effectively, especially among early-career researchers (87% adoption) and in rapidly growing research regions such as China (77%) and Africa (66%). Researchers in all regions see clear benefits, from reducing workload to improving communication, and many express a desire for clear, consistent policy recommendations that would enable more advanced use…(More)”.