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Helpful Places

Government of Canada – DTPR AI Register

AI-Assisted Classification of Clean Technology Grants

Planning & Decision-making

What it collects

Operational data
Anonymized data
  • Proactively disclosed grants and contributions records published by the Government of Canada — publicly available federal funding records describing projects and organisations receiving federal funding. No personal information is included.
Run by
Innovation, Science and Economic Development Canada (ISED)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

Planning & Decision-making

This system uses machine learning to automatically identify and classify federal grants and contributions related to clean technology, replacing time-consuming manual data collection. It analyses publicly disclosed funding records to group projects by clean-technology subsector, helping government program staff track federal investments more efficiently. The system does not process personal information. Federal employees are informed that AI is used in this process.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Proactively disclosed grants and contributions records published by the Government of Canada — publicly available federal funding records describing projects and organisations receiving federal funding. No personal information is included.

Processing

Clustering & Segmentation
  • The system applies unsupervised machine learning to detect patterns and groupings in grant and contribution text data. It identifies clusters corresponding to clean technology projects and subsector categories without relying solely on pre-defined labels, built using free open-source software and trained on historical manually compiled data.

What it does

Deciding (Analytical AI)
Human decides
  • The system classifies grant and contribution records into clean-technology subsectors. Outputs are classifications and identifications used by GC employees to inform and support federal programs; final policy and program decisions remain with human staff.

Outputs

Operational data
Anonymized data
  • Classifications of federal grants and contributions by clean-technology subsector, and identifications of clean technology projects within the proactive disclosure dataset. These outputs are used by GC employees to track and report on federal investments in clean technology. No personal information is produced.

Run by

Innovation, Science and Economic Development Canada (ISED)
  • The Clean Growth Hub, within Innovation, Science and Economic Development Canada, leads the administrative data pillar of the Clean Technology Data Strategy and deploys this AI system to track federal investments in clean technology.

Clean Technology Data Strategy Administrative Data Collection — Government of Canada AI Register

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 classifications are available to GC employees, primarily those in federal programs tracked by the Clean Growth Hub, to inform and support federal clean technology policy and program development.

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 primary users (GC employees). The system is listed on the Government of Canada's public AI register, providing information about how the machine learning model works, what data it uses, and its purpose. Members of the public may consult the AI register entry for details.
  • Right to Be Informed of AI UseGC employees who are primary users of the system are informed that AI is used in this data collection and classification process. The system does not process personal information, so direct notification of affected members of the public is not applicable in the same way.

Risks and safeguards

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