Multilevel Collaborative Governance to Foster Responsible AI Deployment in Cities

Abstract Artificial intelligence (AI) is bringing new governance dynamics in cities. To ensure responsible deployment of this disruptive technology, municipalities throughout the world are putting in place various governance mechanisms. However, scholars and practitioners are more focused on AI governance from a regulations, policies, and strategies perspective, with less emphasis on the actual mechanisms or frameworks that can support cities in responsible AI deployment. Furthermore, the literature tends to overlook areas such as small and medium-sized cities as well as cities in the Global South, particularly in sub-Saharan Africa. By examining the cases of eThekwini in South Africa and Thérèse-De Blainville in Quebec, this article provides a nuanced understanding of how multilevel collaborative efforts can effectively harness AI for urban innovation, ensuring ethical standards and contextual relevance. The research methodology employed involves a comprehensive literature review focusing on the challenges and limitations of responsible AI development in resource-constrained urban environments, alongside an exploration of the concept of multilevel collaborative governance. Additionally, interviews were conducted with city officials and project teams involved in AI tool implementation to gain deeper insights into their strategies and experiences. The analysis reveals that multilevel collaborative governance in AI deployment supports these municipalities in overcoming the technical and financial gaps. Furthermore, this approach also ensures that AI is deployed ethically to address specific challenges and promote responsible urban innovation. However, complex dynamics of power and dependency arise in such collaborative frameworks. The hybrid roles of actors involved in AI deployment including public, private, and nonprofit sectors further complicate the regulatory landscape and require clear frameworks for accountability and responsibility. In exploring these dynamics, this article contributes to the broader discourse on AI governance, offering practical insights for policy makers and urban planners striving to navigate the challenges and opportunities of AI in resource-constrained settings.