Globally, AI governance discussions are undergoing a notable shift. While AI policy has long focused on competitiveness, governments are increasingly recognizing that widespread adoption depends on public trust. As a result, trust has become a defining theme in contemporary AI governance debates.

Canada’s recently released AI strategy reflects this broader trend. Launched on June 4, 2026, AI for All seeks to accelerate AI adoption across the economy while emphasizing the importance of responsible deployment and public trust. Ahead of its release, Minister of Artificial Intelligence and Digital Innovation Evan Solomon described trust as “absolutely vital” to the government’s AI agenda, underscoring the growing view that public trust is essential to widespread adoption. 

The European Union has been at the forefront of this shift, placing trust at the centre of the AI Act through requirements for transparency, accountability, human oversight, and risk management. Likewise, the OECD’s AI Principles and the G7 Hiroshima AI Process have increasingly framed trustworthy AI as a governance objective. Together, these developments reflect a growing recognition that the widespread adoption of AI depends not only on technological capability and economic opportunity. It also depends on public trust in the institutions that govern its use.

Yet this emerging consensus leaves an important question unresolved: what produces trust in practice?  This question is particularly important in Canada. Making AI ubiquitous requires not only encouraging adoption, but also establishing meaningful safeguards, oversight mechanisms, and avenues for accountability. The answer lies in expanding governance beyond the data that AI systems use to include the decisions, assessments, and recommendations they help produce.

More broadly, this reflects a shift from data governance to decision governance. While data governance focuses on how information is collected, stored and used, decision governance concerns how AI-assisted decisions, recommendations and classifications shape decisions affecting individuals. It asks whether those decisions are transparent, explainable, contestable and subject to meaningful human oversight.

From Data Protection to Decision Governance

Privacy and copyright frameworks address different aspects of AI governance. Privacy law governs the........

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