Lead and main contact for ICAI Utrecht: Thomas Dohmen (t.dohmen@uu.nl)

The National Police Lab AI is a collaborative initiative of the Dutch Police, Utrecht University, University of Amsterdam and Delft University of Technology. Together the lab aims to develop state-of-the-art AI techniques to improve the safety in the Netherlands in a socially, legally and ethically responsible way. The researchers in the lab work on techniques across the full breadth of AI. At the University of Amsterdam, machine-learning techniques for extracting the right information from different sources such as photos, text and video are developed. Utrecht University focuses on models from symbolic AI that allow us to reason with and communicate this information.

The RAIL Lab is a research lab dedicated to developing AI technology to increase the overall logistic rail capacity. The lab works towards algorithmic support to ensure safe and reliable logistic operations and capacity planning that is trusted by human experts. The lab’s research goal is to tackle long-term challenges related to the dynamic management of transport demand on railway nodes, with the aim of responding quickly and adequately to changing circumstances. RAIL Lab is a collaboration between Delft University of Technology, Utrecht University, Dutch Railways, and ProRail.

Utrecht AI & Mobility Lab is a collaboration between Utrecht University, ProRail, Nederlandse Spoorwegen (NS) and Qbuzz. The lab focuses on innovative AI techniques for various mobility domains such as public transportation, intelligent infrastructure, and mobility behaviour of the public. Traffic movements of people and goods have grown steadily over the years posing major challenges in terms of safety, accessibility, throughput and emissions. The mission of this lab is to meet these challenges by developing innovative AI techniques. In particular, the mobility lab contributes to the fundamental and applied research by developing and integrating data- and model-driven techniques for the optimization of logistics processes such as train schedules and goods transport, personalized online services, decision-support systems for policy and management in the field of mobility and transport, and the construction of intelligent infrastructure for monitoring and controlling traffic.

Information-driven or data-driven oversight is a prerequisite when wanting to perform as an inspection agency that maximizes its social impact. They face challenges as dealing with the large number of companies, and the complexity and/or amount of information to evaluate when determining compliance lies beyond human capacities. Support from techniques that can manage this information, such as AI, are therefore a requirement for selective, effective and reflective oversight on a strategic and operational level.