To “In-House” or To Outsource? Artificial Intelligence in Canadian Local Governments
Bibliographic record
Abstract
Artificial Intelligence (AI) promises significant benefits to municipalities, such as improved service delivery and operational efficiency. Given the resource-intensive nature of AI, municipalities must decide whether to develop AI in-house or outsource AI systems. Whereas outsourcing decisions have been widely studied in the private sector there is limited research to help us understand how municipalities navigate this choice despite their unique constraints, public accountability, and policy considerations. Our study addresses this gap by surveying representatives of 28 Canadian municipal AI projects to investigate the factors influencing their decisions. We found six factors that influence the decision of municipalities to “in-house” or outsource AI. We found that insufficient in-house AI expertise was the primary determinant of outsourcing. Funding and data sharing issues challenged both in-house development and outsourcing. Ensuring AI explainability and trust is perceived as more challenging when outsourcing. Contrary to common assumptions, AI maintenance is perceived as more difficult when outsourced. The lack of AI-specific regulations poses challenges for in-house development due to limited government guidance but also offers flexibility, while creating challenges in constructing AI outsourcing contracts. This paper is the first to compare in-house AI development and outsourcing within local governments. By capturing firsthand experiences of the participants directly involved in the AI projects, our research provides empirical insights into the trade-offs between these two approaches. Overall, these findings offer valuable guidance for municipalities seeking to make informed AI adoption decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".