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Government of Canada – DTPR AI Register

AI-Assisted Detection of Environmental Extremes in Ocean Data

Ecology · Research & Development

What it collects

About a measurement
Anonymized data
  • Environmental sensor readings covering conditions such as ocean temperature, dissolved oxygen levels, and acidity (pH). These are physical measurements with no link to any individual person.
Run by
Fisheries and Oceans Canada (DFO)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

EcologyResearch & Development

This system uses machine learning to identify unusual environmental conditions — such as extreme temperatures, low oxygen, or high acidity — in ocean and aquatic data managed by Fisheries and Oceans Canada. It segments the data into regions with consistent conditions and defines extremes based on historical variability within each region. The system is used by Government of Canada employees for research and environmental monitoring, and does not process personal information.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Environmental sensor readings covering conditions such as ocean temperature, dissolved oxygen levels, and acidity (pH). These are physical measurements with no link to any individual person.

Processing

Clustering & Segmentation
  • The core algorithm segments ocean and aquatic datasets into regions with internally consistent environmental conditions. Each region's historical variability then defines what counts as an extreme, enabling context-aware identification of anomalous events.
Anomaly Detection
  • Within each segmented region, the system detects environmental extremes by flagging conditions that depart significantly from that region's historical baseline — for example, unusually high temperatures, severe hypoxia, or acute acidification events.

What it does

Deciding (Analytical AI)
Human decides
  • The system classifies ocean data segments and scores environmental conditions as extremes or non-extremes based on historical variability. Outputs are advisory — Government of Canada scientists interpret the results and decide what follow-up analysis or action to take.
Sensing (Perceptive AI)
Human decides
  • The system ingests raw environmental sensor measurements and segments them into structured regions with consistent conditions, converting unstructured observational data into labelled, analysis-ready partitions.

Outputs

About a measurement
Anonymized data
  • The system outputs segmented environmental regions and flagged extreme-condition events — including region boundaries and associated extreme thresholds derived from historical variability. All outputs are aggregate environmental measurements with no personal data.

Run by

Fisheries and Oceans Canada (DFO)
  • Fisheries and Oceans Canada is the federal department responsible for safeguarding Canadian waters and managing aquatic resources. It is developing and deploying this machine learning system to characterize environmental extremes in ocean and aquatic datasets.

GC AI Register — record 2526-DFO-MPO-015

Built by

Government of Canada
  • The Government of Canada developed this machine learning method internally. No external vendor or third-party supplier is identified in the register entry.

GC AI Register — record 2526-DFO-MPO-015

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs are available to Fisheries and Oceans Canada employees and other Government of Canada staff who use the system for environmental research and monitoring. The register identifies primary users as GC employees only.

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 published a summary of this system on the public AI register. Members of the public may review the register entry for information about the system's logic and purpose. Further inquiries may be directed to Fisheries and Oceans Canada.

Risks and safeguards

  • Civil liberties harmThe system does not process personal information and does not make decisions about individuals. Risk to civil liberties is therefore very low. The system is scoped to environmental data only and is used exclusively by government scientific staff, reducing any risk of misuse for surveillance or profiling purposes.