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Helpful Places

Government of Canada – DTPR AI Register

AI-Assisted Code Writing for Software Developers

Employment & Work

What it collects

About behaviour
Anonymized data
  • Code written by the developer, inline comments, file context, and prompts typed into the coding environment. This captures the developer's coding actions and instructions as runtime input to the model.
Operational data
Anonymized data
  • Existing source code files, project structure, and programming language context from the developer's repository or workspace, used to ground suggestions in the operational codebase.
Run by
Fisheries and Oceans Canada (DFO)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Not stated by the Helpful Places.
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Employment & Work

GitHub Copilot is an AI coding assistant being piloted by Fisheries and Oceans Canada (DFO) to help government software developers write code more efficiently. The tool suggests code completions and generates code snippets based on developer input. It is used exclusively by Government of Canada employees and does not involve personal information. This pilot is in development and is intended to evaluate whether AI-assisted coding provides operational value to the department.

What it collects and what happens to it

Data taken in

About behaviour
Anonymized data
  • Code written by the developer, inline comments, file context, and prompts typed into the coding environment. This captures the developer's coding actions and instructions as runtime input to the model.
Operational data
Anonymized data
  • Existing source code files, project structure, and programming language context from the developer's repository or workspace, used to ground suggestions in the operational codebase.

Processing

Language Models
  • GitHub Copilot is powered by large language models (LLMs) specialized for code generation, trained on large corpora of publicly available source code and natural language text. It predicts and generates contextually appropriate code completions.

What it does

Creating (Generative AI)
Human decides
  • GitHub Copilot generates code suggestions, completions, and snippets in response to developer prompts and existing code context. Developers review and decide whether to accept, modify, or discard each suggestion.
Understanding (Semantic AI)
Human decides
  • GitHub Copilot understands the meaning and intent of code, comments, and developer instructions to ground its suggestions in the relevant programming context and retrieve applicable patterns from its training.

Outputs

Generated content
Anonymized data
  • Code completions, function bodies, boilerplate code, and inline suggestions produced by the model in response to developer input. These are AI-generated content artifacts that the developer then reviews and optionally incorporates into the codebase.

Run by

Fisheries and Oceans Canada (DFO)
  • Fisheries and Oceans Canada is the federal department piloting GitHub Copilot to enhance developer productivity in software development operations.

Government of Canada AI Register — 2526-DFO-MPO-005

Built by

GitHub
  • GitHub Copilot is a product developed and supplied by GitHub, a subsidiary of Microsoft. The Government of Canada licenses and deploys this tool as part of its AI pilot program.

Government of Canada AI Register — 2526-DFO-MPO-005

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • This system is used only by Government of Canada employees (DFO developers). It does not collect or process information about members of the public, and the public does not interact with or have access to the outputs of this system.

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 UseGovernment of Canada employees using this tool are informed that they are interacting with an AI-assisted coding system. As a piloted system, users are made aware of the AI nature of the tool through the deployment and onboarding process.
  • Right to Algorithmic TransparencyInformation about how GitHub Copilot works is publicly available through GitHub's documentation and Microsoft's responsible AI disclosures. The Government of Canada's AI register entry provides additional transparency about this deployment.

Risks and safeguards

  • Societal & cultural harmAI-generated code suggestions may embed biases, insecure patterns, or proprietary code from training data, potentially degrading software quality or introducing legal risk.Safeguard: The system is in a pilot phase specifically to evaluate operational value and risks before wider deployment. Developers review all suggestions before acceptance, maintaining human oversight over all code incorporated into production systems.