Stefaan Verhulst

Report by Brookings: “Cities in the U.S. and globally face a severe, system-wide housing shortfall—exacerbated by siloed, proprietary, and fragile data practices that impede coordinated action. Recent advances in artificial intelligence (AI) promise to increase the speed and effectiveness of data integration and decisionmaking for optimizing housing supply. But unlocking the value of these tools requires a common infrastructure of (i) shared computational assets (data, protocols, models) required to develop AI systems and (ii) institutional capabilities to deploy these systems to unlock housing supply. This memo develops a policy and implementation proposal for a “Home Genome Project” (Home GP): a cohort of cities building open standards, shared datasets and models, and an institutional playbook for operationalizing these assets using AI. Beginning with an initial pilot cohort of four to six cities, a Home GP-type initiative could help 50 partner cities identify and develop additional housing supply relative to business-as-usual projections by 2030. The open data infrastructure and AI tools developed through this approach could help cities better understand the on-the-ground impacts of policy decisions, while also providing a constructive way to track progress and stay accountable to longer-term housing supply goals…(More)”.

Book by Winnifred R. Louis, Gi K. Chonu, Kiara Minto, Susilo Wibisono: “Why do some societies evolve and adapt while others remain stagnant? What creates divisiveness and exclusion, and what leads to community cohesion and social progress? This book discusses the psychology of social system change and resistance to change, offering readers a deep exploration of the psychological dynamics that shape societal transformations. Readers explore psychological perspectives on intergroup relations and group processes, alongside interdisciplinary perspectives from environmental science, history, political science, and sociology, to question and challenge conventional thinking. This readable, entertaining book contains clear definitions, lucid explanations, and key learnings in each chapter that highlight the take-home points and implications, so that readers can apply these insights to their real-world challenges…(More)”.

Paper by Suyash Fulay, Sercan Demir, Galen Hines-Pierce, Hélène Landemore, Michiel Bakker: A large share of retail investors hold public equities through mutual funds, yet lack adequate control over these investments. Indeed, mutual funds concentrate voting power in the hands of a few asset managers. These managers vote on behalf of shareholders despite having limited insight into their individual preferences, leaving them exposed to growing political and regulatory pressures, particularly amid rising shareholder activism. Pass-through voting has been proposed as a way to empower retail investors and provide asset managers with clearer guidance, but it faces challenges such as low participation rates and the difficulty of capturing highly individualized shareholder preferences for each specific vote. Randomly selected assemblies of shareholders, or “investor assemblies,” have also been proposed as more representative proxies than asset managers. As a third alternative, we propose artificial intelligence (AI) enabled representatives trained on individual shareholder preferences to act as proxies and vote on their behalf. Over time, these models could not only predict how retail investors would vote at any given moment but also how they might vote if they had significantly more time, knowledge, and resources to evaluate each proposal, leading to better overall decision-making. We argue that shareholder democracy offers a compelling real-world test bed for AI-enabled representation, providing valuable insights into both the potential benefits and risks of this approach more generally…(More)”.

Report by Neil Kleiman, Eric Gordon and Mai-Ling Garcia: “AI is rapidly reshaping the public sector, but most efforts remain focused on optimizing existing processes rather than reimagining how institutions serve communities. If governments continue to pursue efficiency alone, they risk entrenching the very systems that residents already distrust. Based on two years of research—including more than 40 interviews, pilots in Boston, New York City, and San José, and a scan of national policy trends—we propose an alternative framework for public AI adoption: Adapt, Listen, and Trust (ALT).

Rather than reinforce the status quo, the ALT framework guides civic partners to build more responsive public institutions by (1) adapting to the amplified demand AI unleashes, (2) building shared civic infrastructure that enables genuine listening at scale, and (3) cultivating two-way accountability that deepens public trust. The report concludes by outlining concrete recommendations for governments, philanthropy, universities, and community organizations to align around the ALT approach…(More)”.

Paper by Mojgan Askarizade & Ensieh Davoodijam: “This study investigates the dynamics of public sentiment surrounding the 2024 Iranian presidential election by analyzing Persian-language tweets. We introduce the IranElectionTweet dataset, a comprehensive collection of 111,386 election-related tweets enriched with textual content, user metadata, and engagement indicators. Due to the sensitive political context and privacy considerations, the full dataset is not publicly released; instead, we provide a manually annotated subset of 500 tweets (Tweet IDs and dates) for benchmarking, along with reconstruction instructions and analysis code. To conduct sentiment analysis, we fine-tuned GPT-4 on a publicly available Persian sentiment dataset, adapting it to the linguistic and cultural nuances of Persian political discourse. In parallel, we evaluated three cutting-edge large language models, Claude Sonnet 3.7, DeepSeek-V3, and Grok-4, using a few-shot learning framework due to the unavailability of fine-tuning access at the time of experimentation. All models were benchmarked on a manually annotated subset of 500 tweets. DeepSeek-V3 attained the highest weighted F1-score and overall accuracy, indicating stronger performance on the majority classes and was selected as the primary model for sentiment classification. The final sentiment analysis was applied to the full dataset, capturing hourly and daily variations in sentiment and candidate mentions throughout the election period. The results reveal distinct patterns in public opinion corresponding to key political events, offering valuable insights into the real-time evolution of electoral sentiment on social media. This research highlights the effectiveness of advanced multilingual language models in low-resource settings and contributes to the broader understanding of political behavior in digital environments…(More)”.

Article by Sophia Knight: “The development of non-profit, public interest alternatives to access and debate information online can contribute to a healthier information ecosystem – the question is what role should public service media play in providing them? We are living within an increasingly volatile and unpredictable democratic landscape in the UK. We face challenges with political polarisation and social cohesion, exacerbated by the decades-long fragmentation of our civic infrastructure.

The shift to an online information ecosystem has disrupted traditional media. Digital public spaces have enabled almost anyone, anywhere, to speak their minds, opening new avenues for connection. Yet, the open internet has become overrun by sprawling platform monopolies, shaped by algorithms and profit-seeking incentives towards attention and outrage.

Policymakers, and to a large extent the media industry, are stuck on one part of the solution: regulating harmful online content. To move forward, we need to identify and build on opportunities to improve the digital information ecosystem, rather than only targeting potential threats…(More)”.

Paper by Marc E. B. Picavet, Peter Maroni, Amardeep Sandhu, and Kevin C. Desouza: “Generating strategic foresight for public organizations is a resource-intensive and non-trivial effort. Strategic foresight is especially important for governments, which are increasingly confronted by complex and unpredictable challenges and wicked problems. With advances in machine learning, information systems can be integrated more creatively into the strategic foresight process. We report on an innovative pilot project conducted by an Australian state government that leveraged generative artificial intelligence (AI), specifically large language models, for strategic foresight using a design science approach. The project demonstrated AI’s potential to enhance scenario generation for strategic foresight, improve data processing efficiency, and support human decision-making. However, the study also found that it is essential to balance AI automation with human expertise for validation and oversight. These findings highlight the importance of iterative design to develop robust AI tools for strategic foresight which, alongside stakeholder engagement and process transparency, build trust and ensure practical relevance…(More)”.

Update by Yossi Matias et al: “When disasters strike, Google products like Search and Maps help billions of people make critical decisions to stay safe. Our flood forecasting information — now covering more than two billion people — provides life-saving forecasts before the most significant river floods. It’s helped organizations like World Vision get drinking water and food to communities when they need it most. And during the devastating 2025 California wildfires, we provided crisis alerts with information from local authorities to 15 million people across Los Angeles while showing them where to find shelter in Google Maps. This is all made possible by our geospatial AI models, not only for floods and wildfires, but cyclones, air quality and many more.

We recently introduced Google Earth AI, bringing together these geospatial models to help tackle the planet’s most critical needs. Earth AI is built on decades modeling the world, combined with state of the art predictive models and Gemini’s advanced reasoning, letting enterprises, cities and nonprofits achieve deeper understanding in minutes — efforts that previously required complex analytics and years of research.

Today, we’re advancing Earth AI’s innovations and capabilities, and expanding access around the globe. Here’s how:..

To solve a complex problem, you need to see the whole picture, not just one piece of it. That’s the idea behind Geospatial Reasoning, a framework powered by Gemini that now lets AI automatically connect different Earth AI models — like weather forecasts, population maps and satellite imagery — to answer complex questions.

Instead of just seeing where a storm might hit, our latest research demonstrates that analysts can use Geospatial Reasoning to identify which communities are most vulnerable and what infrastructure is at risk, all at once. For example, Geospatial Reasoning empowers the nonprofit GiveDirectly to respond to disasters by combining flood and population density information, helping them identify who needs direct aid most…(More)”.

Report by Columbia World Projects: “Citizens cannot make active choices about what they see on social media. Independent regulators cannot hold companies accountable for their obligations under a growing number of national and regional online safety regimes. The research community — made up of academics, civil society groups and the media — cannot highlight potential deficiencies in both platform and regulatory action. Collectively, it represents a deficit in social media platform transparency and accountability that is a direct threat to individuals’ fundamental rights, as well as to wider societal democratic norms. Funders, regulators and researchers must act within the next 6-12 months to establish foundational infrastructure and standards related to social media data access. Without swift action, democratic institutions are vulnerable to the weaponization of social media platforms whose activities remain opaque and subject to potential manipulation by malign actors.

It is within this context the Columbia-Hertie initiative provides clear funding recommendations, as outlined in the chart below. At its core, this work is based on upholding the highest levels of data protection and security practices so that any form of social media data access protects the privacy rights of individual social media users — no matter where they are located. That is the guiding principle for all recommendations.

The report is divided into three sections:
1. Supporting Underlying Data Access Infrastructure
2. Building Best Practices for the Research Community
3. Fostering Researcher-Regulator Relationships

Each of these sections provide specific recommendations on how public and private funders can meet the existing opportunities within social media data access. The recommendations include which type of funder is most appropriate; how much money is required to meet the objectives; and a time-scale for results…(More)”

Report by James P. Cummings: “Humanity stands at the threshold of a new era in biological understanding, disease treatment, and overall wellness. The convergence of evolving patient and caregiver (consumer) behaviors, increased data collection, advancements in health technology and standards, federal policies, and the rise of artificial intelligence (AI) is driving one of the most significant transformations in human history. To achieve transformative health care insights, AI must have access to comprehensive longitudinal health records (LHRs) that span clinical, genomic, nonclinical, wearable, and patient-generated data. Despite the extensive use of electronic medical records and widespread interoperability efforts, current health care organizations, electronic medical record vendors, and public agencies are not incentivized to develop and maintain complete LHRs. This paper explores the new paradigm of consumers as the common provenance and singular custodian of LHRs. With fully aligned intentions and ample time to dedicate to optimizing their health outcomes, patients and caregivers must assume the sole responsibility to manage or delegate aggregation of complete, accurate, and real-time LHRs. Significant gaps persist in empowering consumers to act as primary custodians of their health data and to aggregate their complete LHRs, a foundational requirement for the effective application of AI. Rare disease communities, leaders in participatory care, offer a compelling model for demonstrating how consumer-driven data aggregation can be achieved and underscore the need for improved policy frameworks and technological tools. The convergence of AI and LHRs promises to transform medicine by enhancing clinical decision-making, accelerating accurate diagnoses, and dramatically advancing our ability to understand and treat disease at an unprecedented pace…(More)”.