AI-Assisted Extraction of Spectrum Licence Conditions

Planning & Decision-making · Enforcement

What it collects

Operational data

Anonymized data

Unstructured spectrum licencing documents from the Spectrum and Telecommunications Sector, sourced from publicly available regulatory records and official publications. No personal information is involved.

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

Planning & Decision-making

Enforcement

This system uses large language models and retrieval-augmented generation to automatically extract and structure spectrum licence conditions from regulatory documents published by Innovation, Science and Economic Development Canada. It is designed for use by Government of Canada employees to reduce manual review work and improve access to critical regulatory information. The system is still in development, does not process personal information, and users are informed when AI is involved.

What it collects and what happens to it

Data taken in

Operational data

Anonymized data

Unstructured spectrum licencing documents from the Spectrum and Telecommunications Sector, sourced from publicly available regulatory records and official publications. No personal information is involved.

Processing

Language Models

Large language models (LLMs) are used to read and interpret unstructured regulatory text and produce structured, machine-readable extractions of spectrum licence conditions.

Search & Retrieval

Retrieval-augmented generation (RAG) is employed to surface relevant regulatory passages from spectrum documents, grounding LLM outputs in authoritative source material for accurate licence condition extraction.

What it does

Creating (Generative AI)

Human decides

Uses large language models to extract, organise, and structure regulatory and technical rules from unstructured documents, producing machine-readable outputs. GC employees review and act on the structured outputs; the system is advisory.

Understanding (Semantic AI)

Human decides

Retrieval-augmented generation is used to find and ground outputs in relevant regulatory passages, enabling accurate extraction of licence conditions from large corpora of spectrum documents.

Outputs

Operational data

Anonymized data

Structured, machine-readable extractions of spectrum licence conditions derived from regulatory documents. Outputs are administrative records about regulatory rules, not about any individual person.

Run by

Innovation, Science and Economic Development Canada (ISED)

The federal department developing and deploying this AI system to automate extraction of spectrum licence conditions from regulatory documents for use by its employees in the Spectrum and Telecommunications Sector.

Automated Information Extraction of Spectrum Licence Conditions — Government of Canada AI Register

Built by

Government of Canada

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

Automated Information Extraction of Spectrum Licence Conditions — Government of Canada AI Register

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me

The system's outputs are intended for internal use by GC employees in the Spectrum and Telecommunications Sector. There is no indication that members of the public can directly access the system or its outputs.

Available to the accountable organization

GC employees at Innovation, Science and Economic Development Canada have access to the structured outputs produced by the 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 Algorithmic TransparencyThe Government of Canada discloses AI use to users of this system. The system is listed on the public Government of Canada AI Register, where its capabilities, data sources, and development status are described.

Risks and safeguards

Societal & cultural harmLLMs may misextract or mischaracterise regulatory conditions, potentially leading to inaccurate spectrum analytics and flawed evidence-based decisions.Safeguard: The system is still in development; outputs are reviewed by GC employees, and AI use is disclosed to all users. The register notes that the approach addresses error-proneness of manual review, suggesting ongoing accuracy validation.