What a Ground-Up AI Governance Strategy Looks Like

Start with Vision & Principles: Define national values and risk-based ethical foundations.

Build Governance Architecture: Multilayer institutions with a mix of voluntary and regulatory mechanisms aligned to international norms.

Ensure Sovereign Infrastructure: Domestic compute capacity and equitable access.

Formalize Safety & Ethics: Compliance standards, audits, and continuous monitoring.

Equip Public Sector: A centralized AI expertise hub and integrated governance practices.

Foster Innovation & Talent: Skills programs, R&D funding, and commercial support.

Engage Public & Stakeholders: Ongoing consultations and accountability paths.

Evaluate & Adapt: Regular review cycles with transparent metrics and international benchmarking.

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Below is a structured, ground-up framework for an AI governance strategy, informed by the Canadian Sovereign AI Strategy context (including Canada’s AI Strategy for the federal public service and Sovereign AI Compute Strategy) and broader AI governance principles. This is not Canada’s official policy but a conceptual AI governance strategy that could be implemented from foundational principles to operational execution.

1. Foundational Vision & Principles

A. National Purpose and Values

A strategy must begin with clear goals aligned to national values:

Human-centric AI that advances public welfare, equity, and democratic values. Sovereignty and resilience in critical AI infrastructure and data. Economic competitiveness in global AI innovation. Trust, safety, ethics, and accountability in AI development and deployment.

B. Core Guiding Principles

Framework principles to shape policy and practice:

Transparency and explainability of AI decisions. Fairness and non-discrimination in AI impact on persons and groups. Safety, security, and privacy by design. Risk-based regulation that distinguishes between low and high-risk applications. Public engagement and inclusivity in governance design. Alignment with international norms and standards to facilitate interoperability and global collaboration. 

2. Governance Architecture

A. Institutional Structures

Establish or empower multi-stakeholder nodes:

AI Governance Council (interministerial body): policy alignment across sectors (health, finance, defence, public services). Technical Advisory Committee: domain experts advising on risk, safety, standards. Public/Community Forum: formal mechanism for civil society and public input.

B. Regulatory Frameworks

Tiered regulatory instruments based on risk and impact:

Voluntary Codes of Responsible Conduct for industry (e.g., existing generative AI code).  Mandatory compliance for high-risk systems, including third-party audits and certification. Algorithmic Impact Assessments for public sector systems. Data protection and privacy laws governing AI data use and cross-border data flows.

C. International Cooperation

Align with global standards and frameworks (e.g., OECD AI Principles, ISO AI standards) to avoid isolation while ensuring interoperability and competitive integration. 

3. Sovereign Infrastructure & Compute Strategy

A. Domestic Compute Capacity

Ensure affordable, secure, and high-performance computing infrastructure that protects Canadian data sovereignty and innovation capacity—including supercomputers, cloud services, and data centres located within the country. 

B. Public-Private Collaboration

Incentivize private investment in sovereign compute infrastructure while maintaining governance safeguards (e.g., data residency and audit controls). 

C. Access Equity Programs

Mechanisms such as compute access funds to support SMEs and researchers, ensuring broad participation and reducing barriers for innovation. 

4. AI Safety, Risk Management, and Ethics

A. Risk Classification

Define risk tiers (low, medium, high) based on potential harm to safety, privacy, fairness, and societal impact.

B. Safety Standards & Certification

Pre-deployment evaluation: compliance checks for high-risk systems. Continuous monitoring: post-deployment auditing and impact reporting.

C. Ethical Frameworks

Adopt an ethical governance framework (e.g., fairness, accountability, non-discrimination, and human oversight) that requires documentation, explainability, and redress processes.

5. Public Sector Standards & Capacity

A. Public Service AI Strategy

Build an AI Centre of Expertise within government to provide training, standard methodologies, and operational support for ethical AI use. 

B. AI Policy Integration

Integrate AI governance tools into digital service delivery, procurement, and automated decision-making frameworks.

C. Open Government & Transparency

Publish AI use policies, impact assessments, and algorithmic decision methodology for public scrutiny to build trust.

6. Innovation, Talent, and Economic Development

A. Education & Workforce Development

National programs to develop AI literacy from K-12 to advanced skills for workers affected by AI adoption.

B. R&D and Commercialization Support

Funding schemes for startups and researchers; tax incentives; national labs and innovation hubs.

C. Responsible Innovation Incentives

Grants and challenges for ethical AI solutions addressing societal needs (healthcare, environment, accessibility).

7. Public Engagement and Accountability

A. Transparent Consultation Mechanisms

Ongoing consultations and feedback loops with citizens, Indigenous communities, and stakeholders to continually inform policy. 

B. Accountability and Redress

Clear mechanisms for grievances, enforcement actions, and remediation where AI systems cause harm.

C. Reporting and Review Cycles

Regular public reporting on AI governance outcomes, updated every 1–2 years to adapt to technological evolution.

8. Monitoring, Evaluation, and Adaptive Governance

A. Metrics and KPIs

Define measurable indicators (e.g., safety incidents, economic impact, adoption rates, fairness outcomes).

B. Adaptive Legal Frameworks

Introduce “sunset clauses” and periodic legislative reviews to ensure laws remain relevant.

C. International Benchmarking

Routine comparison to global peers to identify gaps and emerging best practices.

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