Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy
Seth Bullock, Nirav Ajmeri, Mike Batty, Michaela Black, John Cartlidge, Robert Challen, Cangxiong Chen, Jing Chen, Joan Condell, Leon Danon, Adam Dennett, Alison Heppenstall, Paul Marshall, Phil Morgan, Aisling O'Kane, Laura G. E. Smith, Theresa Smith, Hywel T. P. Williams
TL;DR
Addresses how to engineer AI to support collective action at national scale by integrating five application themes with cross-cutting governance. Proposes the AI4CI Hub and Loop to coordinate data gathering and agent-driven interventions across domains like healthcare, finance, environment, pandemics, and cities, while embedding human-centred design, governance, and responsible innovation. The work identifies unifying challenges—human, technical, and scale—and outlines stakeholder engagement and sustainability guidelines to translate research into real-world impact. The anticipated outcome is scalable, privacy-preserving, and trustworthy AI-enabled coordination for public policy and citizen-facing decision support.
Abstract
Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.