Artificial Intelligence (AI) Adoption in Canadian Local Governments: Opportunities, Challenges and Factors of Innovation
Bibliographic record
Abstract
Artificial Intelligence (AI) has become increasingly prevalent in local governments worldwide, contributing to improved internal administrative processes and service delivery. Local governments serve as the frontline of citizen interactions and are vital to economic development and sustainability, whereas they face resource limitations and struggle to manage AI's high risks. Canada presents an interesting case study, as it is recognized as a leader in AI and invested heavily in AI firms, whereas Canadian governments are generally considered risk-averse. In this thesis, I empirically investigate AI adoption in Canadian local governments with the aim of understanding the aspects that play a crucial role in the successful adoption of AI.I begin by comparing and contrasting Information Technology (IT) and AI adoption in local governments, providing an opportunity to identify whether IT adoption can provide insights into AI adoption. I conclude that although AI presents unique issues, AI and IT adoption share similarities in their promises to local governments and pitfalls around their resource requirements and political influence. In Chapter 3, I present a survey of 28 representatives directly involved in AI projects in Canadian local governments. I highlight positive perceptions of the benefits of AI but also identify challenges related to resources, training, expertise, data and computing infrastructure. In Chapter 4, I examine the innovation factors that contribute to the success of AI adoption in the City of Edmonton, Alberta, Canada, a leader in AI among Canadian cities. I develop a framework that consists of internal and external factors specific to AI innovation in local governments then I apply these factors to Edmonton. The study highlights six internal factors, including AI-specific resources, internal needs, risk-taking culture, collaboration and knowledge sharing, upper management support, and AI process fit, and three external factors encompassing the innovation ecosystem, environmental drivers, and AI regulation and ethics.This thesis contributes to the research on and praxis of local government adoption of AI in several ways. First, it uncovers differences and similarities between traditional IT systems and AI systems in local governments, providing lessons for AI adoption. Second, the thesis offers the first empirical investigation on the current practice of AI in Canadian local government and identifies the challenges they face in adopting AI, providing insights for informed policy decisions and responsible AI implementation. Third, it introduces a framework for measuring AI innovation in the public sector, which aids future analysis of AI innovation and helps local governments understand the necessary conditions for AI innovation. Last, the thesis provides empirical evidence by analyzing AI practices in the City of Edmonton, showcasing how these innovation factors manifest in practice. These findings should guide future AI implementation in other local governments and contribute to research on AI adoption in the public sector
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".