Skip to content
This is NOT an official site of the Government of Canada. Click here for the official AI registry.

Helpful Places

Government of Canada – DTPR AI Register

Back to your search

AI-Assisted Transcription of Insect Collection Labels

Research & Development

What it collects

About a measurement
Anonymized data
  • Photographs of insect specimen labels from Agriculture and Agri-Food Canada's collection (approximately 100,000 images), containing scientific and collection metadata such as species names, collection dates, and locations. No personal information is included.
Operational data
Anonymized data
  • Scientific research papers available on the AAFC network, used to provide contextual reference for interpreting label content such as taxonomic terminology and collection conventions.
Run by
Agriculture and Agri-Food Canada (AAFC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

Research & Development

This system uses generative AI to automatically read and transcribe the labels attached to insect specimens in Agriculture and Agri-Food Canada's scientific collection, covering approximately 100,000 specimens. It extracts text from photographed labels — both printed and handwritten — and structures the content for direct entry into a central insect database. The system is used by Government of Canada employees and does not process any personal information. Users are informed that AI is involved in the process.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Photographs of insect specimen labels from Agriculture and Agri-Food Canada's collection (approximately 100,000 images), containing scientific and collection metadata such as species names, collection dates, and locations. No personal information is included.
Operational data
Anonymized data
  • Scientific research papers available on the AAFC network, used to provide contextual reference for interpreting label content such as taxonomic terminology and collection conventions.

Processing

Language Models
  • Generative AI models process label images to extract and structure text, handling both printed and handwritten content in a predefined output format.
Computer Vision
  • Image recognition capabilities are used to interpret label photographs, supporting extraction of text from both printed and handwritten specimens labels.

What it does

Sensing (Perceptive AI)
Human decides
  • Reads photographed insect specimen labels — both printed and handwritten — extracting text from images using generative AI models.
Creating (Generative AI)
Human decides
  • Uses generative AI models to structure extracted label text into a predefined format for integration with the existing insect database.

Outputs

Operational data
Anonymized data
  • Structured, transcribed label data formatted for direct integration into the central insect specimen database, containing scientific metadata such as species names, collection dates, and locations.

Run by

Agriculture and Agri-Food Canada (AAFC)
  • Federal department responsible for deploying and operating this AI system to digitize the insect collection labels in their scientific collection database.

Government of Canada AI Register — Digitizing Insect Labels

Built by

Government of Canada
  • The system was developed internally by the Government of Canada.

Government of Canada AI Register — Digitizing Insect Labels

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Transcribed data is available to Agriculture and Agri-Food Canada employees through the central insect database.

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 TransparencyAI use is disclosed to users. Government of Canada employees using this system are informed that generative AI is involved in transcribing insect specimen labels. Further information about the system is available through Agriculture and Agri-Food Canada.
  • Right to Be Informed of AI UseUsers (GC employees) are informed that AI is being used in this workflow. The register confirms AI use is disclosed to users.

Risks and safeguards

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