AI Regulation in Canada 2026: PIPEDA, Privacy Commissioner Guidance & What Happened to AIDA

Artificial intelligence is transforming Canadian business—from hiring algorithms to credit decisioning, customer service chatbots to healthcare diagnostics. As AI adoption accelerates, so does regulatory scrutiny. Canada does not currently have a dedicated federal AI statute: the proposed Artificial Intelligence and Data Act (AIDA) died with Bill C-27 in January 2025 and was never enacted.

This guide explains how PIPEDA applies to AI today, what AIDA proposed, and what federal reform is currently before Parliament.

The Canadian AI Regulatory Landscape

Why AI Regulation Matters

Current AI Use Cases:

• Hiring and recruitment screening
• Credit and lending decisions
• Insurance underwriting and claims
• Healthcare diagnostics and treatment recommendations
• Customer service and support
• Fraud detection
• Marketing personalization and targeting
• Pricing optimization

Privacy and Rights Implications:

• Opaque decision-making affecting individuals
• Potential discrimination and bias
• Limited transparency and explainability
• Accountability gaps
• Privacy risks from extensive data processing

Current AI Requirements Under PIPEDA

PIPEDA Already Applies to AI: Even without AI-specific legislation, PIPEDA's principles govern AI use:

1. Consent for AI Processing

• Must obtain appropriate consent for data collection
• Purpose must be identified (AI processing included)
• Sensitive data requires express consent

2. Meaningful Explanation

• Individuals entitled to understand how information is used
• Extends to automated decision-making
• Must be able to explain AI's role in decisions

3. Accuracy Obligation

• Personal information must be accurate, complete, up-to-date
• Applies to training data and AI outputs
• Must correct inaccurate information

4. Accountability

• Responsible for AI systems' handling of personal information
• Can't outsource accountability to vendors
• Must ensure AI complies with privacy principles

Recent Privacy Commissioner Actions:

• Investigation into OpenAI (ChatGPT), launched April 2023 in response to a complaint
• Principles for responsible, trustworthy and privacy-protective generative AI technologies (December 7, 2023)

AIDA: Artificial Intelligence and Data Act (Never Enacted)

Bill C-27, the Digital Charter Implementation Act, 2022, was a government bill in the 44th Parliament. According to the summary in the bill text, it had three parts:

• Part 1 would have enacted the Consumer Privacy Protection Act (CPPA) to govern the protection of personal information in the course of commercial activities. It would have repealed Part 1 of PIPEDA and changed that Act's short title to the Electronic Documents Act.
• Part 2 would have enacted the Personal Information and Data Protection Tribunal Act, establishing an administrative tribunal to hear appeals of certain Privacy Commissioner decisions under the CPPA and to impose penalties for contraventions of certain CPPA provisions.
• Part 3 would have enacted the Artificial Intelligence and Data Act (AIDA) to regulate international and interprovincial trade and commerce in artificial intelligence systems, by requiring certain persons to adopt measures to mitigate risks of harm and biased output related to high-impact AI systems.

Legislative History

From LEGISinfo, Bill C-27 (44-1):

• June 16, 2022: First reading in the House of Commons
• April 24, 2023: Second reading and referral to committee
• Consideration in committee: Not completed
• January 6, 2025: The session ended. Parliament was prorogued, Bill C-27 died on the Order Paper, and it never became law.

What Comes Next: Bill C-36

Federal privacy reform was re-introduced as Bill C-36, An Act to enact the Protecting Privacy and Consumer Data Act, to amend the Personal Information Protection and Electronic Documents Act and to make amendments to other Acts. It was introduced and read a first time in the House of Commons on June 15, 2026, and is at second reading. Its content could change before passage, and it is not yet law. Track it on LEGISinfo, Bill C-36 (45-1).

Privacy Commissioner AI Guidance

OPC's Generative AI Principles

On December 7, 2023, the OPC published Principles for responsible, trustworthy and privacy-protective generative AI technologies. The document applies key privacy principles to generative AI under nine headings: legal authority and consent; appropriate purposes; necessity and proportionality; openness; accountability; individual access; limiting collection, use, and disclosure; accuracy; and safeguards. Read the document itself for the OPC's specific considerations.

Practical AI Privacy Practices

The practices below are general good-practice recommendations, not a summary of the OPC document:

1. Transparency

• Inform individuals when AI is used
• Explain how AI influences decisions
• Provide meaningful information about AI's role
• Make information accessible and understandable

2. Explainability

• Ability to explain AI decisions in plain language
• Understand factors that influenced outcomes
• Meaningful explanations, not just generic descriptions
• Technical documentation for experts, plain language for individuals

3. Fairness and Bias Mitigation

• Proactively assess for discriminatory outcomes
• Test across protected characteristics
• Address discovered bias promptly
• Regular fairness audits

4. Data Minimization

• Collect only data necessary for AI purpose
• Limit use to identified purposes
• Consider privacy-enhancing technologies
• Avoid function creep

5. Accuracy

• Training data must be accurate and representative
• Regular validation of AI outputs
• Correction mechanisms for errors
• Monitoring for model drift

6. Human Review

• Human involvement in significant decisions
• Ability to contest automated decisions
• Override mechanisms where appropriate
• Meaningful review, not rubber-stamping

Key Recommendations

For Organizations Using AI:

• Conduct Privacy Impact Assessments before deploying AI
• Document AI decision-making processes
• Establish governance frameworks
• Regular audits and testing
• Staff training on AI and privacy
• Clear policies on AI use
• Incident response procedures

For High-Risk AI:

• Enhanced scrutiny and oversight
• More rigorous bias testing
• Greater transparency
• Stronger human review
• Regular external audits

Algorithmic Transparency in Practice

Bill C-27 created no legal obligations: the CPPA, including any rules it proposed on automated decision systems, was never enacted. The steps below are practical governance measures, not CPPA or AIDA obligations.

Practical Implementation

1. AI System Inventory

Document all automated decision-making systems:

• System name and purpose
• Decisions made or influenced
• Impact on individuals (legal/significant)
• Data inputs used
• Algorithm type (ML, rules-based, etc.)
• Explainability capability
• Human review process
• Responsibility/ownership

2. Risk Classification

High-Impact:

• Hiring/firing decisions
• Credit adjudication
• Insurance underwriting
• Medical diagnostics
• Criminal justice applications

Medium-Impact:

• Product recommendations
• Content personalization
• Customer service routing
• Marketing targeting (with significant effects)

Low-Impact:

• Basic automation
• Non-consequential recommendations
• Internal efficiency tools

3. Explainability Mechanisms

Technical Documentation:

• Model cards describing AI system
• Training data characteristics
• Features and variables used
• Performance metrics and limitations
• Known biases and mitigation

User-Facing Explanations:

• Plain language descriptions
• Factors that influenced decision
• How to contest or appeal
• Human review process
• Contact information

4. Bias Testing Framework

Pre-Deployment:

• Test AI against protected characteristics
• Disparate impact analysis
• Fairness metrics across groups
• Validate with diverse test data

Ongoing Monitoring:

• Regular bias audits (quarterly/semi-annual)
• Track outcomes by demographic
• Statistical analysis for discrimination
• Alert thresholds for concerning patterns

Remediation:

• Investigate detected bias
• Adjust algorithms or data
• Document mitigation efforts
• Retest after changes

5. Human Review Procedures

When Required:

• High-impact decisions
• Contested automated decisions
• Edge cases or unusual patterns
• Regulatory requirements

Effective Review:

• Reviewer has authority to override
• Access to full context and data
• Ability to correct information
• Not rubber-stamping
• Documented decision rationale

6. Governance Framework

AI Ethics Committee:

• Cross-functional membership
• Reviews high-impact AI deployments
• Assesses risks and mitigation
• Approves/rejects AI projects
• Ongoing oversight

Policies and Procedures:

• AI acceptable use policy
• Bias testing requirements
• Human review standards
• Incident response procedures
• Documentation requirements

Training:

• All staff on AI ethics and privacy
• Developers on bias mitigation
• Reviewers on effective oversight
• Leadership on accountability

Sector-Specific Considerations

Financial Services

Credit Decisioning:

• Enhanced explainability for credit denials
• Adverse action notices with reasons
• Right to contest automated denials
• Human review for borderline cases

Fraud Detection:

• Balance automation with false positives
• Review process for flagged transactions
• Customer recourse mechanisms
• Documentation of AI role

Insurance:

• Underwriting algorithm transparency
• Premium factors disclosed
• Appeals process for denials
• Human review for complex cases

HR Technology

Hiring Algorithms:

• Bias testing across protected characteristics
• Diverse training data
• Human involvement in final decisions
• Explainable selection criteria
• Applicant recourse

Performance Management:

• Transparency about AI in evaluations
• Human judgment in final assessments
• Appeal mechanisms
• Regular bias audits

Healthcare

Diagnostic AI:

• Physician oversight mandatory
• Clear indication of AI involvement
• Explanation of AI recommendations
• Patient consent for AI use
• Regular clinical validation

Treatment Recommendations:

• Decision support, not decision-making
• Physician retains ultimate authority
• Transparency with patients
• Documentation of AI role

E-Commerce and Marketing

Pricing Algorithms:

• Transparency about dynamic pricing
• Avoid discriminatory pricing
• Explanation for significant price differences
• Fair and ethical practices

Recommendation Systems:

• User control over preferences
• Transparency about personalization
• Avoid manipulative practices
• Respect privacy preferences

Compliance Roadmap

Phase 1: Discovery (Month 1-2)

• Inventory all AI systems
• Classify by impact level
• Assess current explainability
• Identify gaps in governance

Phase 2: Risk Assessment (Month 3-4)

• Conduct AI Privacy Impact Assessments
• Bias testing for high-impact systems
• Document risks and mitigation
• Establish governance framework

Phase 3: Remediation (Month 5-8)

• Enhance explainability capabilities
• Implement bias mitigation
• Establish human review processes
• Develop user-facing explanations

Phase 4: Governance (Month 9-12)

• AI ethics committee
• Policies and procedures
• Staff training programs
• Monitoring and auditing

Ongoing: Continuous Improvement

• Regular bias testing
• Monitoring for model drift
• Updates as regulations evolve
• Industry best practice adoption

Conclusion

PIPEDA already applies to AI systems that handle personal information. AIDA and the CPPA were never enacted — Bill C-27 died in January 2025 — and federal reform re-introduced as Bill C-36 is at second reading and not yet law.

Key Takeaways:

• Comply with PIPEDA today when AI processes personal information
• Transparency and explainability are good practice
• Bias testing and mitigation are good practice
• Human oversight for significant decisions
• Documentation supports accountability

Investment in AI governance protects your brand reputation in an increasingly AI-scrutinized market.

Build responsible AI practices today, and follow Bill C-36 as it progresses.