An Empirical Analysis of AI Contributions to Sustainable Cities (SDG11)

Artificial Intelligence (AI) presents opportunities to develop tools and techniques for addressing some of the major global challenges and deliver solutions with significant social and economic impacts. The application of AI has far-reaching implications for the 17 Sustainable Development Goals (SDGs) in general and sustainable urban development in particular. However, existing attempts to understand and use the opportunities offered by AI for SDG 11 have been explored sparsely, and the shortage of empirical evidence about the practical application of AI remains. In this chapter, we analyze the contribution of AI to support the progress of SDG 11 (Sustainable Cities and Communities). We address the knowledge gap by empirically analyzing the AI systems (N 29) from the AI SDG database and the Community Research and Development Information Service (CORDIS) database. Our analysis revealed that AI systems have indeed contributed to advancing sustainable cities in several ways (e.g., waste management, air quality monitoring, disaster response management, transportation management), but many projects are still working for citizens and not with them. This snapshot of AI's impact on SDG11 is inherently partial yet useful to advance our understanding as we move towards more mature systems and research on the impact of AI systems for the social good. Introduction Artificial intelligence (AI) has the potential to mitigate several issues facing cities, such as road safety, waste management, air pollution, and disaster risk reduction (Gupta et al., 2021). Examples of recent AI systems for improved well-being in cities include a tool for semi-automatic digitization of sketch maps to support the inclusion of indigenous communities through the documentation of their land rights (Degbelo et al., 2021; Chipofya et al., 2020), a system for traffic monitoring based on Wireless Signals (Gupta et al., 2018), approaches for efficient waste management (Barns, 2019), air quality modelling (Gupta et al., 2018) and urban health monitoring systems (Allam and Jones, 2020).