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AI-Assisted Asset Code Classification for Capital Expenditure

Planning & Decision-making

What it collects

Operational data
Anonymized data
  • Historical survey write-in descriptions of capital expenditure assets paired with their NAPCS code classifications, used as training and reference data for the recommender system.
About behaviour
Anonymized data
  • Free-text asset descriptions as written by survey respondents, submitted at runtime for code recommendation. These are textual write-ins describing capital expenditure items.
Run by
Statistics Canada (StatCan)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Planning & Decision-making

This tool recommends asset codes for written descriptions of capital expenditure items submitted by survey respondents. It is used internally by Statistics Canada analysts to reduce the time spent manually coding asset descriptions. The system does not process personal information, and analysts review recommendations before finalizing classifications.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Historical survey write-in descriptions of capital expenditure assets paired with their NAPCS code classifications, used as training and reference data for the recommender system.
About behaviour
Anonymized data
  • Free-text asset descriptions as written by survey respondents, submitted at runtime for code recommendation. These are textual write-ins describing capital expenditure items.

Processing

Classification & Prediction
  • A text classification model that maps free-text capital expenditure asset descriptions to NAPCS asset codes, trained on historical write-in and code pairs. Described as a recommender system using text analysis and classification.

What it does

Deciding (Analytical AI)
Human decides
  • The system classifies written asset descriptions into NAPCS asset codes. A human analyst reviews and approves the recommended code before it is applied, meaning the system suggests but does not finalize the classification.

Outputs

A recommendation or prediction
Anonymized data
  • A recommended NAPCS asset code for a given written capital expenditure description. The output is advisory — analysts review and decide whether to accept the recommendation.

Run by

Statistics Canada (StatCan)
  • Statistics Canada developed and deploys this tool for use by Government of Canada analysts to classify capital expenditure asset descriptions with NAPCS codes.

GC AI Register — 2526-StatCan-016

Built by

Government of Canada
  • The system was developed internally by the Government of Canada and is not sourced from an external vendor.

GC AI Register — 2526-StatCan-016

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs (recommended asset codes) are available to Statistics Canada analysts and other Government of Canada employees who use the tool in the survey data processing workflow.
Not available to me
  • This is an internal government tool. Survey respondents and members of the public do not have access to the system outputs or the recommended codes generated for their submissions.

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 Algorithmic TransparencyThe Government of Canada has disclosed the use of this AI tool via the public Algorithmic Impact Assessment Registry. Descriptions of the system's purpose, data sources, and capabilities are publicly available through the Government of Canada Open Data portal.
  • Right to Be Informed of AI UseAI use is disclosed to users of this system. Government of Canada employees using the tool are informed that recommendations are generated by an AI system.

Risks and safeguards

  • Reputational harmIncorrect asset code recommendations could lead to misclassification of capital expenditure data in national statistics, potentially affecting the accuracy of published economic data.Safeguard: human analyst review is required before any recommendation is accepted and applied to the official record.