Tianren Yang
Facilitator
This session is jointly organized by the University of Hong Kong, the World Urban Planning Education Network, and CIDOB’s (Barcelona Center for International Affairs) Global Cities Programme, with the support of Carnegie Endowment for International Peace, Cities Coalition for Digital Rights and Barcelona City Council.
Cities generate vast amounts of data from sensors, social media, mobile apps and administrative records. This urban data holds immense potential for understanding patterns, modelling scenarios and informing policies to address urban challenges. Unlocking insights requires advanced computational techniques at the intersection of data science, urban planning and public policy. At the same time, many cities are piloting AI tools to improve services and tackle issues like congestion and climate change, concerns persist about potential impacts on rights and social risks. There is an urgent need for cities to implement AI ethically.
The focus of this event is twofold. First, it explores computational urban science, which integrates cutting-edge analytics, machine learning and visualisation to drive evidence-based decision-making for sustainable development. Participants will examine how novel approaches like deep learning, agent-based modelling and urban informatics can revolutionise urban planning, governance and management across transport, housing, energy and infrastructure. Through keynotes, panel discussions and demonstrations, attendees will gain insights into computational urban science applications. These include simulating urban growth, integrating diverse data sources for environmental monitoring and enhancing systems thinking.
Second, the event will address the ethical deployment of urban AI. Experts, practitioners and policymakers will engage in discussions about the challenges and opportunities in implementing AI at local levels. The goal is to promote ethical AI systems that are sustainable, fair, accountable, transparent and aligned with democratic values. This session will survey how cities are contributing to the current state of affairs, first by analysing common challenges local governments face when aiming to implement ethically secure AI systems. The event will then delve into case studies and best practices from the most comprehensive openly accessible repository of ethical urban AI initiatives worldwide, GOUAI’s Atlas of Urban AI (led by CIDOB and supported by Barcelona City Council and the Cities Coalition for Digital Rights, among others), and the work of the Cities Coalition for Digital Rights, a network of cities aiding each other in the greenfield of digital rights based policy-making. Policies, strategies and concrete projects will be presented, providing an overview of general trends in urban AI evolution.
This event unites innovators from data science, urban policy, civil society and technology, fostering idea exchange. It explores computational urban science trends, discusses opportunities and addresses challenges. Participants will shape guidelines for responsible urban AI and data-driven approaches optimising sustainability while upholding ethics and equity. By examining potential and pitfalls, the event positions computational urban science as a tool for achieving Sustainable Development Goals and liveable cities.
(1) Provide an overview of how cities and subnational governments are designing and implementing computational urban science and AI technologies.
(2) Analyse challenges in implementing ethically secure AI systems locally and present case studies of ethically deployed urban AI worldwide.
(3) Introduce GOUAI’s Atlas of Urban AI and explore AI benefits while promoting tools that safeguard digital rights.
(4) Share best practices in applying computational urban science to sustainability challenges and educate on data science techniques for evidence-driven urban policymaking.
(5) Examine ethical frameworks for responsible deployment of urban data systems and foster collaboration among policymakers, planners, scientists and community organisations.
(6) Identify capacity gaps and explore opportunities for research and partnerships to accelerate adoption of computational urban science.