Artificial intelligence in local governments: perceptions of city managers on prospects, constraints and choices

Abstract

Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI is on the rise in urban policy circles, there is limited understanding and lack of empirical studies on the city manager perceptions concerning urban AI systems. Bridging this gap is the rationale of this study. The methodological approach adopted in this study is twofold. First, the study collects data through semi-structured interviews with city managers from Australia and the US. Then, the study analyses the data using the summative content analysis technique with two data analysis software. The analysis identifies the following themes and generates insights into local government services: AI adoption areas, cautionary areas, challenges, effects, impacts, knowledge basis, plans, preparedness, roadblocks, technologies, deployment timeframes, and usefulness. The study findings inform city managers in their efforts to deploy AI in their local government operations, and offer directions for prospective research.

Drivers behind the public perception of artificial intelligence: insights from major Australian cities

“…Fifth, the public sees value in what AI currently offers and potentially could bring in urban services and disaster management. Recently conducted studies, by Nili et al ( 2022 ), Sanchez et al ( 2022 ) and Yigitcanlar et al ( 2022b ), revealed insights into public sector experiences about deploying AI and offer a wide spectrum of AI adoption prospects for local governments as creating efficiencies, tackling complexity, managing repetitive tasks, processes, and decisions, automating routine decisions, minimising errors, and improving productivity that particularly benefit customer services, cybersecurity, policy and decision-making, environmental and development control, service and infrastructure management, urban planning, and performance review.…”

Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute

“…An overlapping but distinct area of research focuses on the role of AI in the built environment, so-called urban AI [1,42,56,57,67,73,79]. Many application contexts here are mobility-related, for example smart electric vehicle charging [1]; autonomous vehicles [56]; and automated parking control systems [67].…”

Public Perceptions on Application Areas and Adoption Challenges of AI in Urban Services

“…AI is one of the most disruptive technologies of our time with many powerful applications that cities around the globe have started to take advantage of [17]. According to Yigitcanlar et al [18], "in the context of cities, AI is the engine of automated algorithmic decisions that generate various efficiencies in the complex and complicated local government services and operations. Managing city assets with structural health monitoring, energy infrastructure fault detection and diagnosis, accessible customer service with chatbots, and automated transportation with autonomous shuttle busses are among the many examples of how AI is being utilized in the local government context."…”

“…The presented complex mix of SCs is the foundation of the empirical section detailing the current socioeconomic structuring of AI perceptions (see Section 4). While there are some studies on what urban managers think on the prospects and constraints of AI in the context of local government services [18], there is a knowledge gap in understanding what the public thinks about AI adoption in urban services-especially considering the societal differentiation and digital divides. This study focuses on the socioeconomic variables (see Table 2) and locations (Australia and Hong Kong) to bridge this gap.…”