An Empirical Analysis of AI Contributions to Sustainable Cities (SDG11)
This provides an empirical snapshot for researchers and policymakers on AI's role in sustainable urban development, though it is incremental due to its partial analysis.
The authors tackled the lack of empirical evidence on AI's contributions to sustainable cities (SDG11) by analyzing 29 AI systems from databases, finding that AI has advanced areas like waste management and air quality monitoring, but many projects involve citizens passively rather than collaboratively.
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 AIxSDG 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 social good.