XS projects have a maximum duration of one year and can therefore quickly contribute to scientific insights. The following researchers from Vrije Universiteit Amsterdam have been awarded a grant:
Separating the Privileged Wheat from the Chaff with AI: A Socio-Legal Exploration of AI-Driven Filtering Systems for Protecting Professional Privilege in Criminal Investigations
Dr. M. Galic, Vrije Universiteit Amsterdam
This project investigates the obstacles to protecting digital legal privilege in criminal investigations using AI-based filtering. A high-profile fraud case involving thousands of confidential emails exposed the limitations of traditional keyword filtering. Despite AI’s potential to improve efficiency and accuracy, its application remains largely untapped. Using empirical socio-legal methods—such as interviews and focus groups with police, prosecutors, and lawyers—this study identifies the legal, technical, and organizational requirements for an effective AI filtering system. The groundbreaking research contributes to the ongoing modernization of the Dutch Code of Criminal Procedure and safeguards legal privilege in the digital age.
Off the Beaten Track? A Holistic and Inclusive Approach to Exploring How People Encounter and Interpret Political Information
Dr. T. Groot Kormelink, Vrije Universiteit Amsterdam
This project explores how people come across and interpret political information in today’s complex media landscape. The challenge lies in the fact that each individual is exposed to a unique mix of sources, ranging from newspapers to memes, group chats to livestreams. To address this, the project combines digital trace data (donated by participants from platforms such as Google, Facebook, WhatsApp, and TikTok) with mobile ethnography, allowing participants to reflect on the political content they encounter. Underrepresented groups are deliberately included through a representative sample and targeted recruitment, aiming to improve understanding of how diverse socio-demographic groups engage with and interpret (political) information.
Small Samples, Big Insights: Ethical Sampling of Archaeological Teeth for Precise Isotope Analysis
Dr. L.M. Kootker, Vrije Universiteit Amsterdam
Strontium isotope analysis of teeth is essential for studying human mobility, but it comes with trade-offs. Manual sampling is destructive (requiring tooth extraction) but allows for high precision, whereas laser ablation is less invasive but offers lower accuracy with current equipment. This project tests a portable laser ablation system (pLA) that minimizes damage to tooth enamel while allowing for ultra-precise analysis of the ablated material. This innovative method offers ethical advantages combined with top-level analytical precision, providing a semi-destructive solution for archaeological, museum-based, and forensic human remains.
WolfHowl: Howling with the Wolves. Traditional Ecological Knowledge Practices as Disaster Mitigation Strategies
Dr. L. Oikonomakis, Vrije Universiteit Amsterdam
Traditional Ecological Knowledge (TEK) consists of local practices used to address environmental problems, including disaster prevention. As part of "rewilding Europe," the wolf has returned to the continent—though not without conflicts involving shepherds, hunters, and NGOs. The preventive value of TEK remains underexplored, especially the practice of mimicking wolf howls to track wolf populations. This project studies that practice in Halkidiki, Greece, and its potential as a disaster mitigation strategy, contributing to the growing TEK bibliography.
Towards a High-Resolution Integrated Assessment Model for Assessing Inequalities in Climate Change Impacts and Risks
Dr. M.A. Poblete Cazenave, Vrije Universiteit Amsterdam
Climate change is an urgent global challenge, but its impacts vary greatly by region—often hitting the most vulnerable the hardest. Existing climate-economic models (cost-benefit and process-based IAMs) offer valuable insights but fall short in regional detail and socioeconomic representation. This project aims to develop a hybrid IAM that combines the strengths of both approaches, integrating detailed economic models, climate risk assessments, and downscaled energy and emissions data. By enhancing regional representation and improving the accuracy of climate risk projections, the project will support policymakers in crafting fairer and more effective global climate mitigation and adaptation strategies.
Cooperation Beyond Borders: An AI-Driven Approach to the Study of Human Cooperation Across Cultures
Dr. G. Spadaro, Vrije Universiteit Amsterdam
How and why do people cooperate across different societies? Existing research has yielded inconclusive results, leaving key questions about universal versus culture-specific factors of cooperative behavior unanswered. This project aims to unify existing cross-cultural datasets and apply advanced AI techniques to reveal global social differences and behavioral patterns. It will create a large-scale, machine-readable dataset from thousands of decisions in online behavioral experiments, to be published as open access. By using AI-driven knowledge graphs on this data, the project will generate new insights and contribute to innovative theories of human cooperation.
Measuring Banks' Exposure to Cybersecurity Risks
Dr. R. Wang, Vrije Universiteit Amsterdam
As cyber threats to financial institutions grow, this project focuses on developing a real-time measure of banks' cybersecurity risks using stock market data and machine learning. Unlike traditional retrospective methods, this forward-looking approach enables regulators and financial institutions to better manage risks. The research will create a cyber risk index based on market expectations, assess its impact on bank valuations, and estimate the additional capital needed to withstand severe cyber incidents. The findings will contribute to financial stability and improved management of cybersecurity risks in the digital economy.