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Government of Canada – DTPR AI Register

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AI-Powered Food Label Classification and Monitoring

Environmental Health · Research & Development

What it collects

About a place
Anonymized data
  • Publicly available food product data collected from the internet, including food package label images. The system collects data about products available in the food environment rather than about specific individuals.
About a measurement
Anonymized data
  • Nutrition facts tables and lists of ingredients extracted from food package labels. These are factual numeric and compositional data about food products, with no link to individual persons.
Run by
Health Canada (HC)
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

Environmental HealthResearch & Development

This system collects publicly available food product data from the internet — including nutrition facts tables and ingredient lists — and uses machine learning to classify that data and generate automated reports for Health Canada staff. It does not collect or process personal information. The system is an internal research and monitoring tool used by Government of Canada employees to track the food labelling environment.

What it collects and what happens to it

Data taken in

About a place
Anonymized data
  • Publicly available food product data collected from the internet, including food package label images. The system collects data about products available in the food environment rather than about specific individuals.
About a measurement
Anonymized data
  • Nutrition facts tables and lists of ingredients extracted from food package labels. These are factual numeric and compositional data about food products, with no link to individual persons.

Processing

Computer Vision
  • Optical character recognition (OCR) is used to interpret text on food package label images. This is the perceptive processing step that converts images of labels into machine-readable text fields.
Classification & Prediction
  • Machine learning models classify food product data extracted from labels — assigning category labels to products, nutrient levels, or ingredient types — and produce data models and automated reports for Health Canada analysts.

What it does

Sensing (Perceptive AI)
Autonomous
  • The system uses optical character recognition (OCR) to read and extract text from food package label images collected from the internet, converting them into structured data for downstream classification.
Deciding (Analytical AI)
Human decides
  • Machine learning models classify the extracted food label data and generate automated reports. Human GC employees review and act on these reports; the system's outputs are analytical summaries, not binding decisions about individuals.

Outputs

Operational data
Anonymized data
  • Automated reports and data models describing trends and classifications in the food labelling environment. These outputs are used internally by GC employees and contain no personal information.

Run by

Health Canada (HC)
  • Health Canada is the federal department responsible for this system. FLAIME is deployed and operated by Health Canada to monitor the food labelling environment in Canada.

Government of Canada AI Register — FLAIME

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs and collected data are available to Health Canada (GC employees) who use the system for food environment monitoring. There is no indication data is shared externally.
Not available to me
  • The system does not involve personal information and is an internal government tool. Members of the public are not affected by its outputs and have no access to its data or reports.

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 UseThe register entry notes that AI use is not disclosed to users. As this system processes only publicly available food product data (not personal information) and is used exclusively by GC employees, there are currently no disclosure mechanisms to the public. This right is noted here as a transparency gap.

Risks and safeguards

  • Societal & cultural harmClassification errors in food label data could mislead food policy analysis or misrepresent the nutritional landscape of certain food categories or communities.Safeguard: The system's outputs are reviewed by GC employees before informing policy; no automated decisions affecting individuals are made. Outputs are analytical summaries only.