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AI-Assisted Nutritional Intake Monitoring for Older Adults

Healthcare · Research & Development

What it collects that can identify you

About behaviour
Identifiable data
  • Eating patterns and dietary intake records of older adults, including what and how much is consumed over time. The register states the system does not involve personal information, though dietary behaviour linked to individual participants may implicitly be identifiable in a research context.

GC AI Register — 2526-NRC-CNRC-003

Run by
National Research Council Canada (NRC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Not stated by the Helpful Places.

What it is for

HealthcareResearch & Development

This AI research tool developed by the National Research Council Canada tracks eating patterns in older adults to detect dietary changes that may affect health and cognitive function. It is designed to improve accuracy and reduce the burden of self-reporting compared to traditional monitoring methods. The system is currently in development and is used by Government of Canada employees. Individuals whose nutritional intake is monitored should be aware that, according to the register, AI use is not currently disclosed to those affected.

What it collects and what happens to it

Data taken in

About behaviour
Identifiable data
  • Eating patterns and dietary intake records of older adults, including what and how much is consumed over time. The register states the system does not involve personal information, though dietary behaviour linked to individual participants may implicitly be identifiable in a research context.

GC AI Register — 2526-NRC-CNRC-003

Processing

Classification & Prediction
  • The AI classifies eating patterns and predicts or detects dietary changes in older adults. It uses machine learning to provide higher accuracy than traditional self-reporting methods.

GC AI Register — 2526-NRC-CNRC-003

What it does

Deciding (Analytical AI)
Human decides
  • The system analyzes eating patterns and classifies or flags possible dietary changes. Outputs are advisory; research staff and healthcare professionals review results and decide on any interventions.

GC AI Register — 2526-NRC-CNRC-003

Outputs

A recommendation or prediction
Anonymized data
  • The system produces recommendations or flags about possible dietary changes to improve nutrition. These are advisory outputs used by researchers or health professionals rather than binding decisions.

GC AI Register — 2526-NRC-CNRC-003

Run by

National Research Council Canada (NRC)
  • National Research Council Canada (NRC) is the Government of Canada federal research and technology organization responsible for developing and deploying this AI nutritional monitoring research tool.

GC AI Register — 2526-NRC-CNRC-003

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Not stated by the Helpful Places.

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 UseIndividuals whose eating patterns are monitored by this system have the right to be informed that AI is being used to analyze their dietary data. The register notes that AI use is currently not disclosed to users. As the system moves toward deployment, NRC should establish mechanisms to notify participants that an AI system is in use. For inquiries, contact the National Research Council Canada.
  • Right to Algorithmic TransparencyParticipants and the public have the right to understand in plain language how this AI system analyzes dietary patterns, what factors it considers, and how its outputs are used. The Government of Canada's AI Register entry provides initial transparency. Further technical documentation about the model's logic and accuracy should be made available as the system develops.

Risks and safeguards

  • Psychological harmOlder adults who are monitored but not informed that AI is in use may experience a loss of trust or feel surveilled without consent. The register explicitly notes that AI use is not currently disclosed to users.Safeguard: The system is in development; the research team should establish informed consent procedures and disclosure protocols before any clinical or community deployment. Findings should be reviewed by human researchers before being acted upon.
  • Reputational harmInaccurate classification of dietary patterns in vulnerable older adults could lead to stigmatizing labels or unwarranted concerns about cognitive or physical health.Safeguard: As a research tool in development, outputs should be validated against ground-truth nutritional assessments and human expert review before being used to characterize or report on individuals.