AI-Assisted Code Generation for Government Developers

Employment & Work

What it collects that can identify you

About behaviour

Identifiable data

Code written by GC employee developers, including comments, function signatures, and surrounding context within the development environment, which GitHub Copilot uses to generate suggestions.

Also collects operational data, which is anonymized data.

Run by: Indigenous Services Canada (ISC)
Where: No fixed location
Kept: Not stated by the Helpful Places.
Shared with: Accountable organization, Vendor

What it is for

Employment & Work

This system uses GitHub Copilot to help Government of Canada employees write software code more efficiently. It suggests code completions and generates code snippets based on developer input. The primary users are GC employees working on software development tasks. Citizens are not directly affected by this system, but the quality of government software may be influenced by its outputs.

What it collects and what happens to it

Data taken in

About behaviour

Identifiable data

Code written by GC employee developers, including comments, function signatures, and surrounding context within the development environment, which GitHub Copilot uses to generate suggestions.

Operational data

Anonymized data

Existing codebases, documentation, and software project context provided to GitHub Copilot to ground code generation suggestions in the relevant technical environment.

Processing

Language Models

GitHub Copilot is powered by large language models (LLMs) trained on publicly available code and text, capable of generating, completing, and explaining code across many programming languages.

What it does

Creating (Generative AI)

Human decides

GitHub Copilot generates code suggestions and completions based on developer prompts. A human developer reviews and decides whether to accept, modify, or reject each suggestion.

Understanding (Semantic AI)

Human decides

GitHub Copilot understands the context of existing code and developer comments to produce contextually relevant suggestions, drawing on semantic understanding of programming language constructs and intent.

Outputs

Generated content

Anonymized data

Code suggestions, completions, and explanations generated by GitHub Copilot for GC developer review. Outputs are not about any person and do not carry personal identifiers.

Run by

Indigenous Services Canada (ISC)

Indigenous Services Canada is the federal department deploying GitHub Copilot to support code generation activities for GC employees.

Government of Canada AI Register — Code Generation (2526-ISC-SAC-005)

Built by

GitHub

GitHub, a subsidiary of Microsoft, provides the Copilot AI code generation service that is leveraged by Indigenous Services Canada.

Government of Canada AI Register — Code Generation (2526-ISC-SAC-005)

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization

Code suggestions and generated outputs are available to GC employee developers within Indigenous Services Canada who use the tool in their development workflow.

Available to vendor

GitHub (Microsoft) may have access to usage data and code inputs submitted to the Copilot service as part of the service agreement, subject to applicable data processing terms.

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 TransparencyGC employees using this tool are informed that GitHub Copilot is an AI system. Information about how GitHub Copilot works is publicly available through GitHub's documentation. For questions about the department's use of AI tools, contact Indigenous Services Canada.
Right to Be Informed of AI UseGC employees are made aware that they are using an AI-powered code generation tool (GitHub Copilot) as part of their development environment. This AI register entry provides public disclosure of the system's existence and purpose.

Risks and safeguards

Societal & cultural harmGitHub Copilot may reproduce code patterns that embed security vulnerabilities, insecure dependencies, or biased logic from its training data.Safeguard: GC employees are expected to review all generated code before use; standard software development review processes (code review, testing, security scanning) apply to AI-generated code as they would to human-written code.