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Government of Canada – DTPR AI Register

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AI-Powered Course Recommender for Public Servants

Education & Learning · Marketing & Personalization

What it collects that can identify you

About behaviour
Pseudonymous data
  • Learner behaviour data — such as courses viewed, enrolled in, completed, and feedback provided — is used as input to infer preferences and generate personalized recommendations.
Sensitive personal information
Pseudonymous data
  • Learner profile data — which may include role, department, language preference, and skill assessments — is used to tailor recommendations to each Government of Canada employee.

Also collects operational data, which is anonymized data.

Run by
Canada School of Public Service (CSPS)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization, Vendor

What it is for

Education & LearningMarketing & Personalization

This system uses artificial intelligence to suggest learning courses tailored to each Government of Canada employee's profile and past learning behaviour. It combines collaborative filtering and skill graph data to surface relevant training options on the Canada School of Public Service platform. The system is still in development and users are informed that AI is being used. Personal information about learners is processed to generate these recommendations.

What it collects and what happens to it

Data taken in

About behaviour
Pseudonymous data
  • Learner behaviour data — such as courses viewed, enrolled in, completed, and feedback provided — is used as input to infer preferences and generate personalized recommendations.
Sensitive personal information
Pseudonymous data
  • Learner profile data — which may include role, department, language preference, and skill assessments — is used to tailor recommendations to each Government of Canada employee.
Operational data
Anonymized data
  • Course metadata — titles, descriptions, skill tags, prerequisites, and catalogue structure — is ingested as non-personal operational data to support recommendation matching.

Processing

Recommendation & Ranking
  • Collaborative filtering compares each learner's profile and behaviour to those of similar learners to rank courses by predicted relevance, combining this with skill graph signals.

What it does

Deciding (Analytical AI)
Human decides
  • The model scores and ranks courses for each learner, producing a prioritized list. Employees retain full autonomy to select or ignore any recommendation.
Understanding (Semantic AI)
Human decides
  • A skill graph integration component maps learner competencies and course content semantically, linking related skills and topics to improve recommendation relevance.

Outputs

A recommendation or prediction
Pseudonymous data
  • The system outputs a ranked list of course recommendations displayed to each individual GC employee on the CSPS learning platform. The output advises rather than compels — employees choose which courses to take.

Run by

Canada School of Public Service (CSPS)
  • The Canada School of Public Service (CSPS) is the department responsible for deploying and operating this recommender model on its learning platform for Government of Canada employees.

Canada AI Register — Recommender Model

Built by

Microsoft Azure
  • Microsoft Azure is identified as the vendor that supplied or developed components of the recommender model used by CSPS.

Canada AI Register — Recommender Model

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Recommendation outputs and learner interaction data are available to the Canada School of Public Service for platform management and model improvement.
Available to vendor
  • Microsoft Azure, as the vendor supplying infrastructure and model components, may have access to data processed through its platform services. The scope of vendor data access is not detailed in the register entry.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Be Informed of AI UseUsers are informed that AI is being used to generate course recommendations. The register entry confirms AI use is disclosed to users.
  • Right to Algorithmic TransparencyAs a Government of Canada system, employees may request information about how the recommender model works through their department's privacy coordinator or the Canada School of Public Service.

Risks and safeguards

  • Reputational harmRecommender model may systematically surface or suppress certain courses for employees based on inferred attributes, potentially creating inequitable learning pathways.Safeguard: The system is in development; CSPS developed it in part internally alongside Microsoft Azure, suggesting iterative quality review. Recommendations are non-binding and employees may browse the full catalogue independently.
  • Loss of autonomyEmployees who rely solely on AI recommendations may miss courses outside their inferred profile, creating filter-bubble effects in professional development.Safeguard: The system surfaces recommendations as suggestions rather than mandatory assignments; the full course catalogue remains accessible to all GC employees.