Canada Algorithmic Impact Assessment Tool

Summary

The Canadian government's Algorithmic Impact Assessment (AIA) tool is a mandatory questionnaire that federal departments must complete before deploying automated decision systems. What sets this tool apart is its practical, risk-based approach that produces concrete impact levels (I through IV) with corresponding requirements for documentation, consultation, and oversight. Rather than offering vague guidelines, the AIA provides a clear framework that determines everything from whether you need a privacy impact assessment to how often you must review your system's performance.

Who this resource is for

• Federal government employees in Canada who are procuring, developing, or deploying automated decision systems
• Government contractors and vendors working with Canadian federal departments on AI systems
• Provincial and municipal governments looking to adapt the framework for their own jurisdictions
• International government officials studying concrete examples of algorithmic accountability in practice
• Policy researchers analyzing how governments operationalize AI governance beyond high-level principles

The four-tier impact system explained

The AIA's core innovation is its scoring system that translates abstract risks into actionable requirements:

Impact Level I (Minimal impact): Simple automation with limited consequences. Requires basic documentation and annual reviews. Example: automated email sorting systems.

Impact Level II (Moderate impact): Systems affecting individual outcomes but with available recourse. Demands consultation records, quarterly reviews, and basic bias testing.

Impact Level III (High impact): Significant decisions affecting individuals or groups. Triggers requirements for privacy assessments, external audits, and detailed algorithmic specifications.

Impact Level IV (Very high impact): Systems with potential for serious harm or Charter rights implications. Mandates deputy minister approval, continuous monitoring, and comprehensive public transparency measures.

How the assessment actually works

The tool asks 48 questions across four domains, each weighted differently based on potential harm:

Project details (15% weight): What type of decision is being automated, who is affected, and what data is being used.

Algorithm impact (25% weight): How the system makes decisions, whether it involves machine learning, and if it affects vulnerable populations.

Data sources (25% weight): Quality, representativeness, and sensitivity of input data, plus data collection methods.

Consultation and oversight (35% weight): What consultation occurred, what appeal mechanisms exist, and how performance will be monitored.

Your total score determines not just your impact level, but triggers specific mandatory requirements like algorithmic audits, public consultations, or senior executive approval.

What happens after you complete it

Unlike many assessment tools that end with recommendations, the Canadian AIA creates binding obligations:

• Documentation requirements: Higher impact levels must publish detailed system descriptions, including algorithmic logic and training data sources
• Approval gates: Level III and IV systems require deputy minister sign-off before deployment
• Review schedules: Mandatory reassessment timelines ranging from annual (Level I) to continuous monitoring (Level IV)
• Transparency obligations: Public posting of impact assessments and system performance metrics
• Recourse mechanisms: Required appeal processes and human review capabilities for higher-risk systems

Lessons from five years of implementation

Since 2019, federal departments have completed hundreds of AIAs, revealing both strengths and challenges:

What works well: The tool successfully identifies high-risk systems and creates accountability mechanisms. Departments report that the structured approach helps them think through risks they might otherwise miss.

Common stumbling blocks: Teams often struggle with scoring questions about algorithmic interpretability and defining "vulnerable populations." The consultation requirements can significantly extend project timelines.

Practical adaptations: Some departments now complete preliminary AIAs during system design rather than just before deployment, allowing earlier course corrections.

The tool has influenced similar frameworks in other countries and provinces, demonstrating how concrete assessment mechanisms can make AI governance principles operational rather than aspirational.