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AI-Assisted Data Processing and Analysis for Statistical Programs

Research & Development · Planning & Decision-making

What it collects

Operational data
Anonymized data
  • Structured and unstructured datasets used for statistical production, including administrative records, survey responses, and operational datasets maintained by Statistics Canada.
About a place
Anonymized data
  • Satellite imagery and address data are processed to extract geographic and location-based statistical information for programs such as land-use analysis and census address parsing.
Run by
Statistics Canada (StatCan)
Where
No fixed location
Kept
Retained not specified in register
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Research & DevelopmentPlanning & Decision-making

Statistics Canada applies machine learning techniques to process structured and unstructured data for statistical programs. These techniques include automatic classification, outlier detection, missing data imputation, address parsing, natural language processing, and satellite image analysis. The primary users are Government of Canada employees who use these models to improve the quality and efficiency of national statistics. Citizens are indirectly affected when these models influence the statistical outputs that inform public policy.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Structured and unstructured datasets used for statistical production, including administrative records, survey responses, and operational datasets maintained by Statistics Canada.
About a place
Anonymized data
  • Satellite imagery and address data are processed to extract geographic and location-based statistical information for programs such as land-use analysis and census address parsing.

Processing

Classification & Prediction
  • Machine learning algorithms are used for automatic classification, outlier detection, and modeling and imputation of missing data across Statistics Canada datasets.
Computer Vision
  • Satellite image processing is applied to extract structured data from remote-sensing imagery for statistical purposes such as land-use estimation and agricultural surveys.
Language Models
  • Natural language processing techniques are applied to unstructured textual data to support classification, address parsing, and extraction of structured information from free-text survey responses or administrative records.
Optimization
  • Robotic process automation is used to automate repetitive data-processing tasks, optimizing statistical production workflows within Statistics Canada.

What it does

Deciding (Analytical AI)
Human decides
  • Models classify, score, predict, and rank data records as part of statistical processing pipelines. Outputs such as imputed values, classifications, and outlier flags are reviewed by GC employees before informing official statistics.
Sensing (Perceptive AI)
Human decides
  • Natural language processing and satellite image processing capabilities turn raw unstructured inputs — text documents, remote-sensing imagery — into structured fields usable downstream in statistical pipelines.

Outputs

Operational data
Anonymized data
  • Outputs include classified records, imputed data values, outlier flags, parsed addresses, and processed statistical datasets used downstream in official statistics production. These outputs are not tied to identified individuals.
A recommendation or prediction
Anonymized data
  • Models produce predictions and recommended imputations for missing data, which GC employee statisticians review and may accept or override before inclusion in official outputs.

Run by

Statistics Canada (StatCan)
  • Statistics Canada is the Government of Canada department responsible for producing national statistics. It deploys these machine learning models to support statistical data processing and analysis programs.

Algorithmic Impact Assessment Registry — Machine Learning Models (2526-StatCan-011)

Built by

Government of Canada
  • The Government of Canada developed these machine learning models internally, as indicated in the register. No external commercial vendor is identified as the system developer.

Algorithmic Impact Assessment Registry — Machine Learning Models (2526-StatCan-011)

Kept for

Retained not specified in register
  • The register does not specify a retention duration for model outputs or processed datasets. Statistics Canada is subject to the Government of Canada's information management policies, but specific retention periods are not disclosed in this entry.
  • Duration: not specified in register

Shared with

Available to the accountable organization
  • Model outputs and processed data are available to Statistics Canada and authorized Government of Canada employees for statistical production purposes.
Not available to me
  • Individual citizens do not have direct access to the model outputs, which are internal to Statistics Canada's statistical production process. Final published statistics are available publicly, but not the intermediate model results.

Stored

Stored locally
  • As a federal government statistical agency, Statistics Canada is expected to store data within Canadian jurisdiction in compliance with federal information management requirements, though the register entry does not explicitly confirm storage location.
  • Duration: not specified in register
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 TransparencyInformation about Statistics Canada's use of machine learning in statistical production is disclosed through the Government of Canada's Algorithmic Impact Assessment Register. Citizens may consult the register entry at the source URL for general information about how these techniques are applied.

Risks and safeguards

  • Reputational harmMisclassification or incorrect imputation by ML models could introduce systematic errors into official statistics, potentially misrepresenting population groups in published data.Safeguard: GC employee review of model outputs before publication, and ongoing model validation and quality assurance processes.