AI and Cities: Risks, Applications and Governance

Activity

AI is a disruptive technology that offers a plethora of opportunities. This report
presents an ambitious overview of some of the strategic applications of AI. Taking a risk-based approach, it also raises awareness of the risks of using AI, regardless of the application. The aim is to provide local authorities with the tools to assess where, and whether, AI could be valuable and appropriate, rather than instructing on what is or is not the right opportunity for a given context.
Cities and local authorities provide crucial areas for AI applications and policymaking because they regularly make day-to-day decisions about AI and how it affects people’s lives. The emergence of AI technologies offers new ways to better manage and equip cities (UN-Habitat 2020, 180). However, the reshaping of cities
through technology and innovation needs to reflect citizens’ needs and, where
possible, be used as a tool to foster more equal prosperity and sustainability.
Cities, towns and settlements may have less policy and risk assessment capacity
than nation states.
This report is part of UN-Habitat’s strategy for guiding local authorities in supporting
people-centred digital transformation processes in their cities or settlements. It
is a collaboration with Mila–Quebec Artificial Intelligence Institute, a community of
more than 1,000 researchers dedicated to scientific excellence and the development of responsible AI for the benefit of all. UN-Habitat helps build safe, resilient,
inclusive and sustainable communities in over 90 countries by working directly
with partners to respond to the UN Sustainable Development Goals (SDGs), and
SDG 11 in particular. Together, this Mila–UN-Habitat collaboration offers a vision and
understanding of how responsible AI systems could support the development of
socially and environmentally sustainable cities and human settlements through
knowledge, policy advice, technical assistance and collaborative action.

Description

Cited work:

Caliva, Francesco, Fabio Sousa De Ribeiro, Antonios Mylonakis, Christophe Demazirere, Paolo Vinai, Georgios Leontidis, and Stefanos Kollias (2018).
A deep learning approach to anomaly detection in nuclear reactors. In 2018
International Joint Conference on Neural Networks (IJCNN),Rio de Janeiro:
IEEE, pp. 1–8. https://doi.org/10.1109/ IJCNN.2018.8489130

Period	Oct 2022
Work for	The United Nations Human Settlements Programme, Global
Degree of Recognition	International