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Responsible AI · Canada
Make AI useful without losing accountability.
Responsible AI is not a separate project. It is how AI is trained, deployed and supervised every day: who decides, what data is used, how outputs are checked, and who owns the outcome.
Our approach
Ethics that are operational, not academic.
Governance that exists only as a PDF does not change what happens when someone uses AI on a Tuesday afternoon.
We help organizations turn principles into specific practices: training people on how to use AI responsibly in their role, and designing systems where permissions, review, logging and escalation enforce the rules automatically.
What we build in
Ten practical commitments.
Applied in proportion to the risk of each use case. A meeting summary does not need the controls of a system that affects someone’s job, benefits or access to services.
Human oversight
Decide which decisions AI should never make alone, where approval is required, and what reviewers are actually checking.
Privacy
Limit AI to the information it needs, respect how personal information was collected, and keep sensitive data out of the wrong tools.
Explainability, appropriate to the use case
A drafting assistant and a system that affects someone's eligibility need very different levels of explanation. Match the standard to the stakes.
Bias and fairness testing
Test for uneven performance and disparate impacts where outcomes affect people, before launch and as systems change.
Data quality
Unreliable, outdated or conflicting information produces unreliable AI. Quality is a responsibility issue, not just a technical one.
Safety and security
Guard against misuse, data leakage and manipulation of AI systems, and limit what agents can access and do.
Provenance and auditability
Know where an answer came from, which sources and model were used, and who approved the outcome.
Correction and escalation
Make it easy to flag errors, fix them at the source and escalate the cases AI should not handle.
Accountability
Every AI-supported outcome has a named owner. Automation changes how work is done, not who is responsible for it.
Policy translated into system behaviour
Rules become permissions, review steps, logging and escalation paths, so governance works without relying on memory.
The questions we help answer
Responsible AI starts with specific questions.
Clear answers to these questions, for each use case, do more than any statement of principles.
- What decisions should AI not make alone?
- Where is human approval required?
- What information can an AI system access?
- What data should not be used at all?
- How is source authority established?
- How are outputs checked and corrected?
- How are bias and disparate impacts evaluated?
- Who is accountable for an automated outcome?
- How are systems monitored as models and data change?
- How does policy become permissions, workflows, logging and escalation?
Canadian context
Grounded in how Canada approaches privacy and accountability.
Canadian organizations work within federal private-sector privacy law, separate provincial regimes in Quebec, Alberta and British Columbia, sector-specific rules, and for federal institutions, the Directive on Automated Decision-Making. Quebec’s privacy law includes transparency obligations for decisions based exclusively on automated processing.
We reflect these realities in training and system design, and point to guidance from bodies such as the Office of the Privacy Commissioner of Canada. We are not a law firm, and we work alongside your counsel and privacy lead where legal interpretation is needed.
AI built for CanadaQuestions
Responsible AI FAQ
What is responsible AI?
Responsible AI is the practice of designing, deploying and operating AI so it is useful, fair, secure, respectful of privacy and accountable. In practice it means clear human oversight, appropriate data use, testing, provenance, correction and named ownership of outcomes.
Is Canada regulating AI?
Canada's approach continues to evolve. The proposed Artificial Intelligence and Data Act did not become law. Existing privacy laws, sector rules and, for federal institutions, the Directive on Automated Decision-Making already shape how AI can be used. Organizations should confirm current obligations with counsel.
Can you make us compliant?
Trained is not a law firm or auditor and does not certify compliance. We help organizations turn their obligations and policies into practical training, system design, controls and workflows, working alongside legal and privacy advisors where needed.
Do we need an AI policy before we start?
A short, practical policy helps, but it does not need to be perfect first. We often develop policy alongside the first training and use cases, so it reflects how AI is actually used in your organization.
Keep exploring
AI Operations
Workflows where review, escalation and accountability are designed in.
AI Training
Responsible-use training built around your roles, tools and policies.
Where does AI need guardrails in your organization?
Tell us how AI is being used today and what worries you. We will help you make it useful and accountable.