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Government of Canada – DTPR AI Register

AI-Assisted Qualitative Analysis of Public Opinion Research

Research & Development · Planning & Decision-making

What it collects

About behaviour
Anonymized data
  • Public opinion research responses: answers given by members of the public in surveys and related studies. The register indicates no personal information is involved, suggesting responses are treated as anonymised or de-identified.
Operational data
Anonymized data
  • Related datasets including Library and Archives studies used alongside public opinion research responses to provide context for qualitative analysis.
Run by
Innovation, Science and Economic Development Canada (ISED)
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

Research & DevelopmentPlanning & Decision-making

This system uses large language models to analyse responses from public opinion research — surveys and similar studies — collected by the Government of Canada. It helps government employees process large volumes of qualitative data more quickly and consistently, reducing the need for a second human analyst. The system is currently in a pilot phase and is used internally by federal employees; members of the public are not directly subject to automated decisions, but their survey responses are processed by the AI. AI use is disclosed.

What it collects and what happens to it

Data taken in

About behaviour
Anonymized data
  • Public opinion research responses: answers given by members of the public in surveys and related studies. The register indicates no personal information is involved, suggesting responses are treated as anonymised or de-identified.
Operational data
Anonymized data
  • Related datasets including Library and Archives studies used alongside public opinion research responses to provide context for qualitative analysis.

Processing

Language Models
  • Large language models (LLMs) are applied to structured and semi-structured qualitative data to increase efficiency and reduce bias in the analysis of public opinion research responses.

What it does

Understanding (Semantic AI)
Human decides
  • Uses large language models to extract meaning, themes, and patterns from qualitative public opinion research responses. Government analysts review and act on the outputs.
Deciding (Analytical AI)
Human decides
  • Classifies and scores qualitative data to reduce bias and increase consistency across large files of public opinion research responses, with human analysts reviewing findings.

Outputs

A recommendation or prediction
Anonymized data
  • Qualitative analysis outputs — themes, patterns, and insights derived from public opinion research — provided to GC employees to support decision-making. These are advisory outputs reviewed by human analysts, not binding automated decisions.

Run by

Innovation, Science and Economic Development Canada (ISED)
  • The federal department that deploys and operates this AI pilot to analyse public opinion research responses collected by the Government of Canada.

GC AI Register — 2526-ISED-ISDE-017

Built by

Government of Canada
  • The system was developed by the Government of Canada. No external vendor is identified in the register entry.

GC AI Register — 2526-ISED-ISDE-017 (Developed by field)

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs and data produced by the system are available to Government of Canada employees (primary users) within Innovation, Science and Economic Development Canada.
Not available to me
  • Members of the public whose survey responses are analysed do not have individual access to the system's outputs or the analyses produced from their responses.

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. This system is listed in the Government of Canada's public AI register (ID: 2526-ISED-ISDE-017), which is publicly accessible. Individuals may consult the register for general information about how the system operates.
  • Right to Be Informed of AI UseThe register confirms that AI use is disclosed to users (GC employees who use the system). Members of the public participating in opinion research surveys are not directly notified through this system, but AI use is publicly acknowledged in the register.

Risks and safeguards

  • Societal & cultural harmLLM-based analysis of public opinion research may introduce systematic biases in how public views are characterized and summarized, potentially distorting policy insights.Safeguard: The system is designed to reduce bias relative to manual analysis; it is in a pilot phase allowing for evaluation before broader deployment; and human analysts review outputs. The need for a second analyst is reduced but human oversight is maintained.
  • Reputational harmIf LLM outputs mischaracterize public opinion themes derived from identifiable communities or groups, this could result in reputational or policy harm to those groups.Safeguard: The register states no personal information is involved and the system focuses on aggregate qualitative themes. Human analyst review of outputs provides a check on mischaracterization before insights are used in decision-making.