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AI-Assisted Extraction of Wildlife Trade Permit Records

Enforcement · Ecology

What it collects that can identify you

Sensitive personal information
Identifiable data
  • Scanned CITES permit PDFs contain personal information about permit holders and applicants (names, addresses, and other identifying details on trade permits). The register confirms personal information is involved.

Also collects about a measurement, which is anonymized data.

Run by
Environment and Climate Change Canada (ECCC)
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

EnforcementEcology

This system uses optical character recognition and a large language model to convert scanned PDF copies of CITES wildlife trade permits into structured digital database records. It is used internally by Government of Canada employees in the Enforcement Branch and Canadian Wildlife Service. Personal information is involved, but affected individuals are not informed that AI is being used to process their permit documents.

What it collects and what happens to it

Data taken in

Sensitive personal information
Identifiable data
  • Scanned CITES permit PDFs contain personal information about permit holders and applicants (names, addresses, and other identifying details on trade permits). The register confirms personal information is involved.
About a measurement
Anonymized data
  • Scanned CITES permit PDFs also contain non-personal regulatory data including species names, quantities traded, permit dates, origin and destination countries, and permit numbers.

Processing

Computer Vision
  • OCR (optical character recognition) is applied to scanned PDF permit images to extract text prior to LLM processing. The system is described as using Vision-Language model capabilities.
Language Models
  • A large language model (LLM) processes the OCR-extracted text to identify, extract, and structure permit data fields into database records, and supports querying of those records.

What it does

Sensing (Perceptive AI)
Human executes
  • Optical character recognition (OCR) converts scanned CITES permit PDF documents into machine-readable text, which is then passed to the language model for structured extraction.
Understanding (Semantic AI)
Human executes
  • A large language model (LLM) interprets the OCR-extracted text from permit documents, identifies and extracts structured fields, and supports querying of the resulting digital records.

Outputs

Operational data
Anonymized data
  • Structured digital database records extracted from CITES permit PDFs, including species trade information, permit metadata, and regulatory compliance data.
Sensitive personal information
Identifiable data
  • The digitized records include personal information about permit holders extracted from the scanned documents, such as names and contact details associated with wildlife trade permits.

Run by

Environment and Climate Change Canada (ECCC)
  • A federal department of the Government of Canada operating the system in collaboration between its Enforcement Branch and the Canadian Wildlife Service to digitize CITES permit records.

Government of Canada AI Register — 2526-ECCC-008

Built by

Government of Canada
  • The system was developed internally by the Government of Canada, with no external vendor identified in the register entry.

Government of Canada AI Register — 2526-ECCC-008

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Output database records are available to GC employees in the Enforcement Branch and Canadian Wildlife Service for enforcement and compliance purposes.
Not available to me
  • Permit holders and members of the public cannot access the AI-generated digital records directly. AI use is not disclosed to affected individuals.

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 explicitly states that AI use is not disclosed to users (AI use disclosed to users: N). Permit holders whose documents are processed by this AI system are not currently informed. This right is not being fulfilled. Affected individuals may contact Environment and Climate Change Canada for information about how their permit data is handled.
  • Right to Correct Your DataIndividuals whose personal information is extracted and stored in the CITES permit database may have rights to request correction of inaccurate records under Canada's Privacy Act. The register does not describe a specific correction mechanism for AI-extracted data.

Risks and safeguards

  • Reputational harmOCR and LLM extraction errors could result in inaccurate records tied to a permit holder's identity, potentially misrepresenting their compliance history. The system is described as in development; mitigations such as human review of extracted records before they enter the production database should be in place. The register does not describe specific mitigation measures.
  • Civil liberties harmThe system processes personal information from enforcement-related permit documents without informing affected individuals that AI is being used (AI use disclosed to users: N). This raises due-process concerns if AI-extracted records inform enforcement decisions. No specific mitigation is documented in the register; transparency and individual notification mechanisms would reduce this risk.