Project MUSE®: Canadian Public Policy - Latest Articles
https://muse.jhu.edu/journal/438
Project MUSE®: Latest articles in Canadian Public Policy.daily12026-05-19T00:00:00-05:00text/htmlen-USVolume 35 (2009) through current issueLatest Articles: Canadian Public PolicyTWOProject MUSE®Canadian Public Policy1911-99170317-0861Latest articles in Canadian Public Policy. Feed provided by Project MUSE®AI Innovation, Safety, and Regulation in Canada: Perspectives from the Editorial Committee
https://muse.jhu.edu/article/990089
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On 26 September 2025, Evan Solomon, Canada's Minister of AI, announced the launch of an AI Strategy Task Force and a 30-day national sprint (ISED 2025) to establish Canada's approach to artificial intelligence (AI). The emphasis was on scaling up AI industry, driving adoption, increasing trust, and establishing sovereignty (Karadeglija 2025b). This announcement was soon followed by specific support for AI strategies through the 2025 federal budget. The 2025 federal budget earmarked $925.6 million over the next five years to support the development of sovereign public AI infrastructure through enhanced computing availability and capacity for private and public research (see Steven 2025). It also featured AI as one
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallAI Innovation, Safety, and Regulation in Canada: Perspectives from the Editorial Committee2026-05-19text/htmlen-USAI Innovation, Safety, and Regulation in Canada: Perspectives from the Editorial Committee2026-05-192026TWOProject MUSE®1521232026-05-19T00:00:00-05:002026-05-19Replace, Reimagine, Recombine: Building an Artificial Intelligence Nation
https://muse.jhu.edu/article/990090
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Labour Productivity Growth for Canada and Other G7 CountriesNote: Growth in gross domestic product per hour of work for Canada and the other G7 countries measured in US$ constant prices, converted using 2015 purchasing power parity.Source: Organisation for Economic Co-operation and Development Productivity Indicators.Canada is in a productivity crisis. Over the past 50 years, its gross domestic product (GDP) per hour worked has grown more slowly than that of any other G7 country (Figure 1). Recently, Canada has not only fallen behind in relative terms but also in absolute terms, with real GDP per capita down more than 1.7 percent over the past three years.1 Its future prospects are no better: a recent Organisation
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallReplace, Reimagine, Recombine: Building an Artificial Intelligence Nation2026-05-19text/htmlen-USReplace, Reimagine, Recombine: Building an Artificial Intelligence Nation2026-05-192026TWOProject MUSE®1825072026-05-19T00:00:00-05:002026-05-19Role of Complementary Capabilities in Artificial Intelligence Adoption and Productivity: Firm-Level Evidence from Canada
https://muse.jhu.edu/article/990091
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Artificial intelligence (AI) is not a new phenomenon; from what to watch on Netflix to what to buy on Amazon, it is prevalent in people's daily lives. However, the new wave of AI since the launch of OpenAI's ChatGPT in November 2022 has democratized this technology, making it accessible to non-specialists and triggering a substantial rise in research and development (R&D) in AI patenting and applications (WIPO 2024).The enthusiasm over AI is not unwarranted given its promising potential to enhance productivity. Projections for AI's contribution to total factor productivity (TFP) growth range from a ten-year accumulative growth of 0.5–0.7 percent (Acemoglu 2025) to 1.5 percent in the United States (Goldman Sachs
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallRole of Complementary Capabilities in Artificial Intelligence Adoption and Productivity: Firm-Level Evidence from Canada2026-05-19text/htmlen-USRole of Complementary Capabilities in Artificial Intelligence Adoption and Productivity: Firm-Level Evidence from Canada2026-05-192026TWOProject MUSE®1753202026-05-19T00:00:00-05:002026-05-19What Lessons Should Canada Take from the Design of Public Data Exchanges?
https://muse.jhu.edu/article/990092
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Government agencies across the world are increasingly adopting algorithmic systems to assist in a wide array of public service areas. Redden and Feldman (2024) report that, in Canada alone, there were 303 artificial intelligence (AI) applications within government agencies, covering tasks such as predicting the likely outcomes of tax litigation, identifying child sexual assault material, and sorting temporary visa and study permit applications.1 This growing reliance on algorithmic decision-making marks a profound shift in how the public is governed. However, as algorithms become more deeply embedded in governmental processes, significant concerns have emerged regarding the specific algorithms being used: their
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallWhat Lessons Should Canada Take from the Design of Public Data Exchanges?2026-05-19text/htmlen-USWhat Lessons Should Canada Take from the Design of Public Data Exchanges?2026-05-192026TWOProject MUSE®1078112026-05-19T00:00:00-05:002026-05-19Developing an Efficient Governance Framework for Synthetic Health Data for Canada
https://muse.jhu.edu/article/990093
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Recognizing the critical role of artificial intelligence (AI) in health research and innovation, Canada established the Pan-Canadian AI Strategy, which includes a specific focus on AI for Health, as early as 2017 (ISED 2024b). This strategy aims to position Canada as a global leader in AI advancement by encouraging innovations and practical adoptions. In particular, the AI for Health initiative supports the development and application of AI technologies to improve patient outcomes, streamline health care operations, and facilitate data-driven decision-making (CIFAR n.d.). However, one of the main challenges to fully realizing AI's potential in health care is the limited access to high-quality health data. Privacy
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallDeveloping an Efficient Governance Framework for Synthetic Health Data for Canada2026-05-19text/htmlen-USDeveloping an Efficient Governance Framework for Synthetic Health Data for Canada2026-05-192026TWOProject MUSE®1951472026-05-19T00:00:00-05:002026-05-19Challenges in Evaluating the Relationship between Trust and Misinformation Using the Canadian Internet Use and Canadian Perspectives Surveys: Implications for Canadian Policies
https://muse.jhu.edu/article/990094
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The objective of this article is to estimate for Canada the relationship between measures of an individual's self-reported trust in government and the likelihood that the same individual reports sharing information on the internet without checking accuracy or verifying information. This is accomplished using the 2022 Canadian Internet Use Survey (CIUS) and the 2020 Canadian Perspectives Survey Series (CPSS). Artificial intelligence (AI) methods, and especially generative AI (GenAI), have resulted in the proliferation of publicly available and unregulated tools that can facilitate the creation and sharing of false information and fake content in vast quantities. The negative impacts of mis- and disinformation are
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallChallenges in Evaluating the Relationship between Trust and Misinformation Using the Canadian Internet Use and Canadian Perspectives Surveys: Implications for Canadian Policies2026-05-19text/htmlen-USChallenges in Evaluating the Relationship between Trust and Misinformation Using the Canadian Internet Use and Canadian Perspectives Surveys: Implications for Canadian Policies2026-05-192026TWOProject MUSE®1596942026-05-19T00:00:00-05:002026-05-19Canadian Policy Responses to Artificial Intelligence–Generated Threats to Democratic Elections
https://muse.jhu.edu/article/990095
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The rapid development and adoption of generative artificial intelligence (AI) have prompted fears of harm to democracy. A review of 50 national elections held in 2024 found that 80 percent saw incidents of generative AI; 69 percent of all incidents appeared to use AI for a "harmful role in the election" (Trauthig et al. 2025). In Canada's 2025 federal election, misleading AI-generated content about candidates and election processes had millions of views (DFRLab 2025; Lavigne et al. 2025). Although AI-generated or AI-targeted content may not single-handedly upend elections (Simon and Altay 2025), AI tools are being developed and used globally to manipulate, misinform, and harass voters and other electoral
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallCanadian Policy Responses to Artificial Intelligence–Generated Threats to Democratic Elections2026-05-19text/htmlen-USCanadian Policy Responses to Artificial Intelligence–Generated Threats to Democratic Elections2026-05-192026TWOProject MUSE®1494652026-05-19T00:00:00-05:002026-05-19Move Deliberately and Build Things: A Framework for Trustworthy Adoption of Artificial Intelligence in Canadian Public Services
https://muse.jhu.edu/article/990096
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Governments around the world and at every level are exploring the deployment of artificial intelligence (AI) tools to deliver services, enhance productivity, and drive government efficiency. At the same time, the global legislative landscape has yet to produce extensive regulatory instruments to govern AI outside of the notable cases of the European Union's AI Act (European Union 2024), the Republic of Korea's AI Basic Act, and a handful of nonbinding international compacts and voluntary codes.1 In Canada, the proposed Bill C-27 containing the Artificial Intelligence and Data Act (AIDA; Canada, Parliament 2022) is now dead (Arai 2025),2 and the Ontario government's Bill 194, Strengthening Cyber Security and
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallMove Deliberately and Build Things: A Framework for Trustworthy Adoption of Artificial Intelligence in Canadian Public Services2026-05-19text/htmlen-USMove Deliberately and Build Things: A Framework for Trustworthy Adoption of Artificial Intelligence in Canadian Public Services2026-05-192026TWOProject MUSE®1432152026-05-19T00:00:00-05:002026-05-19Artificial Intelligence Regulation as Service Value in Public and Private Sectors
https://muse.jhu.edu/article/990097
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This article postulates that service value, stemming first from the adoption of artificial intelligence (AI) service innovations, is not only based on increased service efficiency and productivity but simultaneously comes from regulatory frameworks that ensure AI service innovation is developed and deployed safely and responsibly. Safe and responsible AI service design and deployment brings value-in-use to the beneficiaries because it reflects globally accepted values such as fair data, data privacy, and end-use risk mitigation.Regulatory intervention as it relates to technological proliferation is not new in the case of AI. In fact, it is one of the three layers that envelop an evolutionary economics perspective.
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallArtificial Intelligence Regulation as Service Value in Public and Private Sectors2026-05-19text/htmlen-USArtificial Intelligence Regulation as Service Value in Public and Private Sectors2026-05-192026TWOProject MUSE®1963582026-05-19T00:00:00-05:002026-05-19Risk, Retention, and the Algorithmic Institution: Artificial Intelligence as a Policy Response to Higher Education in Crisis
https://muse.jhu.edu/article/990098
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Faced with mounting fiscal pressure and declining enrollments, post-secondary institutions are increasingly turning to artificial intelligence (AI) and algorithmic systems, particularly predictive early warning systems (EWS), to identify students at risk of attrition and to optimize resource allocation, although comprehensive data on the extent of adoption across the sector remains limited (McConvey and Guha 2024; Perdomo et al. 2023).1Here we distinguish among AI as a broad field of research and discourse; AI technologies, which refer to technical methods such as machine learning, natural language processing, or computer vision; and AI systems, which are socio-technical applications of these technologies in
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallRisk, Retention, and the Algorithmic Institution: Artificial Intelligence as a Policy Response to Higher Education in Crisis2026-05-19text/htmlen-USRisk, Retention, and the Algorithmic Institution: Artificial Intelligence as a Policy Response to Higher Education in Crisis2026-05-192026TWOProject MUSE®1487952026-05-19T00:00:00-05:002026-05-19An Economic Case for Regulating Artificial Intelligence and Implications for Policy
https://muse.jhu.edu/article/990099
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The conversation about AI regulation often starts from a place of fear. Fear that AI-engineered, humanity-destroying microbes will be unleashed by non-state actors; that authoritarian regimes will weaponize AI to entrench control; that the singularity is coming, bringing massive inequality in its wake; and that our children will face a world with few meaningful jobs. I share every one of these fears.But as a professional economist, I cannot ground my response to AI in fear. My responsibility is to approach these challenges using the discipline's analytical tools. That begins with a simple question: Why regulate a technology that holds the potential to revolutionize health care, to accelerate the green transition
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Project MUSE®https://muse.jhu.edu/2026-05-19T00:00:00-05:00https://muse.jhu.edu/journal/438/image/coversmallAn Economic Case for Regulating Artificial Intelligence and Implications for Policy2026-05-19text/htmlen-USAn Economic Case for Regulating Artificial Intelligence and Implications for Policy2026-05-192026TWOProject MUSE®512672026-05-19T00:00:00-05:002026-05-19