AI-Assisted Inspection Scheduling for Weights and Measures

Enforcement · Planning & Decision-making

What it collects

Operational data

Anonymized data

Historic inspection records and compliance data from Measurement Canada systems, covering past inspection outcomes, device registration, and compliance status. The register indicates 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

Enforcement

Planning & Decision-making

This system helps Measurement Canada decide where and when to conduct inspections of weighing and measuring devices in the marketplace. It uses machine learning to predict which devices or areas pose the highest risk of non-compliance with the Weights and Measures Act. The system is used by government employees to prioritize enforcement activity — it does not directly affect members of the public, though businesses subject to inspection may be influenced by its outputs.

What it collects and what happens to it

Data taken in

Operational data

Anonymized data

Historic inspection records and compliance data from Measurement Canada systems, covering past inspection outcomes, device registration, and compliance status. The register indicates no personal information is involved.

Processing

Classification & Prediction

Machine learning-based predictive analytics assess non-compliance risk for weighing and measuring devices, drawing on historic inspection records and compliance data.

What it does

Deciding (Analytical AI)

Human decides

Uses machine learning to score and rank devices and geographic areas by non-compliance risk, producing prioritized recommendations for inspection scheduling. Government employees make the final decision on where and when to conduct inspections.

Outputs

A recommendation or prediction

Anonymized data

Prioritized list of high-risk areas and overdue devices recommending where and when inspections should occur. Outputs are advisory — government employees make final scheduling decisions.

Run by

Innovation, Science and Economic Development Canada (ISED)

Measurement Canada, a branch of Innovation, Science and Economic Development Canada, deploys this algorithm to support compliance inspections under the Weights and Measures Act.

Weights and Measures Marketplace Monitoring Algorithm — Government of Canada AI Register

Built by

Government of Canada

The Government of Canada developed this algorithm internally.

Weights and Measures Marketplace Monitoring Algorithm — Government of Canada AI Register

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me

This system's outputs are used internally by Measurement Canada employees for inspection planning. They are not accessible to the public or to businesses subject to inspection.

Available to the accountable organization

Inspection scheduling recommendations are available to Measurement Canada employees (GC employees) who use them to determine enforcement priorities.

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 has publicly disclosed the existence and general purpose of this algorithm through the Algorithmic Impact Assessment register. Further details about the system's logic may be requested through Access to Information processes.

Risks and safeguards

Civil liberties harmThe algorithm determines which businesses or areas are prioritized for enforcement inspections, which could result in disproportionate scrutiny of certain regions or device types if the training data reflects historical enforcement biases.Safeguard: The register notes that AI use is disclosed, and the algorithm is advisory — human inspectors make final decisions. Measurement Canada should review outputs for geographic or sector-level disparities.
Financial & business harmBusinesses targeted by inspection as a result of algorithmic prioritization may face compliance costs, fines, or reputational effects if the algorithm produces false positives or reflects biased patterns in historical data.Safeguard: The system is advisory; human inspectors exercise judgment before any enforcement action. Businesses may contest inspection findings through existing regulatory appeal mechanisms under the Weights and Measures Act.