A Government Roadmap for Smart, Safe, Ethical AI
The federal government aims to enhance productivity by launching artificial intelligence (AI) “at scale.” This initiative includes modernizing public service with AI tools, which is generally seen as a positive step. Predictive AI models could help anticipate shifts in health care trends, improve fiscal forecasting, and assist in detecting tax fraud, among other applications.
Additionally, Natural Language Processing tools could facilitate larger consultations regarding government decisions. However, these opportunities come with caveats: without a thoughtful implementation and skilled leadership, there is a risk of misusing public funds on superficial tools rather than achieving meaningful progress.
Complexity and Oversight of AI Systems
AI systems are not “set-it-and-forget-it” tools; they are complex, dynamic systems that raise significant concerns about privacy, ethics, and accountability. To operate effectively, these systems require diverse teams of experts—ranging from algorithm auditors to ethics advisors.
The pace of AI innovation is rapid, paralleling the evolution of management and governance techniques, including new strategies for bias mitigation and privacy protection. If AI solutions are launched hastily and without sufficient in-house expertise, governments risk falling behind the very technologies they aim to regulate.
Building Capacity in Government Departments
While establishing a centralized AI hub is a critical step, it is equally essential to build capacity within government departments. Many departments are working to enhance their capabilities, yet the speed of AI development and the level of oversight required present challenges for teams assessing tools, managing risks, and determining the appropriate deployment of AI.
This initiative is not about transforming departments into technology laboratories; rather, it focuses on ensuring that AI decisions are informed by knowledge and operational realities.
Environmental Considerations
Another critical aspect to consider is AI’s substantial carbon footprint. A government-wide AI rollout, conducted without regard for emissions, could undermine national climate commitments. The federal government has recognized this challenge in its new Sovereign AI Compute Strategy, which aims to build Canadian-controlled computing capacity powered by clean energy. This is a crucial step that requires sustained follow-through.
The environmental impact should be a central design constraint rather than an afterthought. This entails favoring energy-efficient models, establishing infrastructure in areas with abundant clean energy, and being selective about the contexts in which AI is employed.
Governance and Oversight of AI Systems
The credibility of AI modernization efforts hinges on ensuring that productivity gains do not come at the expense of climate goals or digital sovereignty. Each AI deployment must be governed by robust and transparent oversight. A growing set of policies, regulations, and institutions—including the AI and Data Act, the Artificial Intelligence Safety Institute, and the Artificial Intelligence Advisory Council—is essential to ensure that AI systems are transparent, accountable, and safe across the economy.
As AI tools are integrated into government operations, the same level of oversight must be applied. This includes publicly disclosing how systems function, the risks they pose, and the mechanisms for monitoring their use.
In Canada, an upfront risk assessment is necessary through an algorithmic impact assessment, as mandated by the Treasury Board Directive on Automated Decision-Making. However, policy instruments need accountability mechanisms and independent bodies, like the Office of the Privacy Commissioner, to investigate non-compliance, which is typically reserved for legislative frameworks.
Strengthening Public Confidence
At a time when trust in public institutions is waning, transparency is not merely an option; it is a necessity. Building on initial steps is vital for boosting public confidence and improving systems.
These challenges illustrate that, without integrated AI leadership, departments risk relying on off-the-shelf solutions or becoming overly dependent on consultants. This can lead to high costs and unsustainability for policy-driven AI tools that require regular updates. Each ministry must develop internal leadership capable of aligning AI initiatives with departmental goals and evaluating where AI can be effectively and safely utilized.
Creating a Network Model of AI Leadership
It is imperative that every major government department establishes the capacity to manage AI adoption by appointing a chief AI officer. These officers would oversee AI development, implementation, and governance, sharing knowledge to foster accelerated learning, all in coordination with the centralized AI Hub.
This network model of AI leadership ensures that subject-matter expertise informs technical decisions, enabling the government to make more deliberate choices about AI usage.
The Road Ahead
Canada’s rich history in AI research is a testament to its creativity and academic rigor. However, excellence in research does not guarantee the safe or effective deployment of AI in the public sector, as cautioned by some of the most respected AI pioneers.
The path forward is clear: it calls for targeted and deliberate modernization that embeds AI knowledge, balances innovation with ethical and democratic principles, and incorporates environmental impacts as essential design constraints.
This approach would empower the government to modernize selectively and strategically, enhancing services without compromising equity, accountability, or sustainability. Anything less risks trading taxpayer dollars for a series of costly experiments with uncertain benefits or, worse, obvious hazards.
