Trust Is Not a Strategy: Why Canada Must Govern AI Decisions, Not Just Data

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 collection, retention, and use of personal information, while copyright law regulates access to and reproduction of creative works. Yet both are primarily concerned with governing access to information. While these issues remain important, they do not fully address the governance challenges that arise when AI systems influence decisions affecting individuals.

This shift has important implications for how trust is understood. Concerns about consent, data protection, and intellectual property remain important, but they address only part of the governance challenge. Questions of transparency, explainability, accountability, oversight, and recourse become increasingly significant when AI systems influence decisions about employment, credit, insurance, education, healthcare, and access to public services.

European and Canadian Approaches to AI Governance

The question, then, is how governments have responded to these emerging governance challenges. As early as 2019, the European Union was framing AI as a decision-making technology whose societal impacts require governance beyond traditional data protection. 

While privacy remains a cornerstone of European digital policy, the EU’s regulatory framework has expanded beyond traditional data protection. It increasingly addresses the consequences of algorithmic decision-making through requirements for transparency, accountability, human oversight, and risk management. Rather than treating AI primarily as a question of data governance, the EU AI Act seeks to address some of the institutional and governance challenges raised by AI-enabled decision-making.

Canadian AI policy discussions have historically followed a different trajectory. Debates have tended to focus on privacy protection, intellectual property, innovation policy, and economic competitiveness. These issues remain important, particularly as governments seek to balance AI adoption with digital sovereignty and economic growth. Compared with European policy debates, however, less attention has been devoted to the institutional safeguards needed to oversee how AI outputs and recommendations are used in practice. As AI systems become embedded across workplaces, financial services, healthcare and public administration, these governance questions are becoming increasingly difficult to avoid. As a result, Canadian AI policy discussions have often placed greater emphasis on innovation and economic competitiveness than on questions of transparency, accountability, human oversight, and contestability. 

Beyond Surveillance: Governing Algorithmic Management

The workplace provides a useful illustration of the governance challenges raised by AI-enabled decision-making. Much of the public debate surrounding algorithmic management technologies focuses on the collection of information about worker behaviour, productivity, location, and workplace activities. These concerns are important, particularly where monitoring is extensive or intrusive. Yet they capture only part of the challenge posed by AI-enabled management systems.

Increasingly, workplace data is used not merely to record employee activity but to evaluate, rank, and manage workers. In sectors such as logistics, warehousing, ride-hailing, and platform work, algorithmic systems are used to generate productivity scores, assess performance, allocate tasks, recommend disciplinary measures, and identify perceived operational risks. Employees may understand that data is being collected while having little visibility into how assessments are produced or how they influence managerial decisions. They may also have limited ability to determine whether those assessments accurately reflect workplace realities. Similar technologies are increasingly appearing in Canadian logistics, retail and platform work, making these governance challenges increasingly relevant domestically.

European studies have highlighted a range of psychosocial risks associated with algorithmic management systems, including, work intensification, reduced autonomy, heightened stress, continuous performance pressure. Researchers and policymakers have also raised concerns about the limited opportunities workers often have to understand, challenge, or participate in decisions shaped by algorithmic management systems.

In response, European policymakers have increasingly sought to strengthen worker protections in algorithmically managed workplaces. The European Union’s Platform Work Directive, for example, introduces new requirements governing automated monitoring and decision-making systems. It further provides workers with opportunities to obtain explanations of significant decisions and request human review in certain circumstances. These measures reflect a broader effort to ensure that algorithmic management systems remain subject to oversight rather than operating as opaque mechanisms of workplace control.

The issues raised by algorithmic management extend well beyond the workplace. As AI systems increasingly shape sectors of insurance, education, healthcare, and public services, attention is increasingly turning to the institutional safeguards needed to oversee how AI-enabled decisions are used in practice.

Building the Conditions for Trust

The challenge facing policymakers is therefore not simply how to accelerate AI adoption, but how to create the conditions under which AI systems can be regarded as legitimate and trustworthy. Recent European initiatives, including the AI Act and the Platform Work Directive, suggest that this requires more than technical standards or regulatory compliance. It requires governance frameworks that impose obligations on organizations deploying AI systems, establish mechanisms for human review and challenge, and equip public institutions with the authority to monitor, investigate and respond to harmful outcomes.

For Canada, this means moving beyond a narrow focus on data governance and developing mechanisms that govern how AI-generated assessments and recommendations are used in practice. At a minimum, effective AI governance requires that individuals affected by significant AI-enabled decisions be informed when AI systems are being used. It requires meaningful explanations of how decisions are reached and accessible mechanisms through which outcomes can be reviewed or challenged. Organizations deploying AI in high-impact contexts should be required to assess and mitigate risks, document decision-making processes, and maintain human oversight where significant rights or interests are affected. 

Trust cannot be secured through adoption targets alone. While Canada’s AI strategy seeks to increase business adoption of AI from 12 percent to 60 percent by 2034, confidence in AI ultimately depends on whether institutions are capable of overseeing how AI systems are used, addressing harmful outcomes, and providing meaningful avenues for review and redress. If Canada aims to embed AI across the economy, governance must develop alongside deployment. Trust is not created through adoption targets or voluntary principles. It is earned through institutions capable of explaining, overseeing and correcting the decisions AI increasingly helps to make.