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AI-Assisted Text Classification for Census Feedback

Research & Development

What it collects

About behaviour
Anonymized data
  • Open-text comments written by census respondents about their census experience or subject matter concerns. The register confirms no personal information is involved, so comments are treated as de-identified feedback.
Run by
Statistics Canada (StatCan)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

Research & Development

This system automatically sorts written comments submitted by census respondents into subject-matter categories using natural language processing and deep learning. It is used by Statistics Canada employees to process large volumes of open-text feedback more efficiently. The system does not handle personal information, and respondents are informed that AI is used in this process.

What it collects and what happens to it

Data taken in

About behaviour
Anonymized data
  • Open-text comments written by census respondents about their census experience or subject matter concerns. The register confirms no personal information is involved, so comments are treated as de-identified feedback.

Processing

Language Models
  • Deep learning-based natural language processing is used to process and classify free-text census respondent comments. The register specifies deep learning and text classification as the core capabilities.
Classification & Prediction
  • Text classification assigns each respondent comment to a subject matter area or general census theme. This is a multi-class classification task using deep learning on labeled historical census comment data.

What it does

Deciding (Analytical AI)
Human decides
  • The system classifies and scores census respondent comments by assigning them to subject matter areas or general census themes. GC employees (primary users) review and act on these classifications — the AI provides categorization to support human analysis rather than making autonomous decisions.
Understanding (Semantic AI)
Human decides
  • Natural language processing is used to understand the meaning and subject matter of free-text census comments before classification. The semantic layer extracts topical intent from unstructured text.

Outputs

About behaviour
Anonymized data
  • Each census comment is assigned a subject matter area or general census theme label. These classifications are used by Statistics Canada employees to analyze patterns in respondent feedback. No personal information is attached to the output.

Run by

Statistics Canada (StatCan)
  • Statistics Canada is the federal department responsible for producing statistics that help Canadians better understand the country. It deploys and operates the Census Comments Classifier to support internal processing of census respondent feedback.

Census Comments Classifier — Government of Canada AI Register Entry 2526-StatCan-013

Built by

Government of Canada
  • The system was developed internally by the Government of Canada, meaning the same public-sector body is both developer and deployer. There is no third-party vendor.

Census Comments Classifier — Government of Canada AI Register Entry 2526-StatCan-013

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • The classified output is available to Statistics Canada employees (GC employees), who are the primary users of the system. There is no indication the output is shared externally.

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 UseCensus respondents are informed that AI is used in the processing of their comments. The register confirms that AI use is disclosed to users (AI use disclosed to users: Y).
  • Right to Algorithmic TransparencyThe Government of Canada's AI Register entry provides public disclosure that this system uses deep learning and natural language processing to classify census comments. Respondents can consult the public register for information on how the system operates.

Risks and safeguards

No risks or safeguards have been published for this system yet.