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AI-Assisted Alignment Analysis for Development Programming

Planning & Decision-making · Research & Development

What it collects

Operational data
Anonymized data
  • Annual and end-of-project reports, management summaries, and CFO Stats from Global Affairs Canada's agriculture and food development programming portfolio. These are administrative project documents describing outputs, activities, and outcomes at the project level.
Run by
Global Affairs Canada (GAC)
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-makingResearch & Development

This system uses machine learning to extract and classify information from Global Affairs Canada project reports, assessing how Canada's agriculture and food development programs align with the Ceres2030 framework for ending hunger. It processes unstructured project documents to identify intervention types and outcomes, supporting portfolio analysis and decision-making by Government of Canada employees. The system does not process personal information and has been retired from active use.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Annual and end-of-project reports, management summaries, and CFO Stats from Global Affairs Canada's agriculture and food development programming portfolio. These are administrative project documents describing outputs, activities, and outcomes at the project level.

Processing

Language Models
  • Custom-trained NLP pipelines extract structured data — intervention types and outcomes — from unstructured project reports and management summaries.
Classification & Prediction
  • Classification models and predictive modeling assess intervention effectiveness and map extracted project features to the Ceres2030 framework categories, with future predictive analysis capability planned.

What it does

Deciding (Analytical AI)
Human decides
  • Classifies and scores project outputs and activities against Ceres2030 intervention categories using classification models and semantic matching; results are presented in dashboards for human decision support rather than automated decisions.
Understanding (Semantic AI)
Human decides
  • Uses semantic matching to map extracted project characteristics and intervention descriptions from unstructured reports to the structured Ceres2030 high-impact intervention taxonomy.

Outputs

Operational data
Anonymized data
  • Structured data on intervention types, outcomes, and alignment scores for Canada's agriculture and food development projects, integrated into dashboards for portfolio visualization and decision support by GC employees.
A recommendation or prediction
Anonymized data
  • Alignment assessments and causal relationship analyses indicating the extent to which interventions individually and jointly meet Ceres2030 strategies and strategic, programmatic, and policy needs — advisory outputs for GC employee decision-making.

Run by

Global Affairs Canada (GAC)
  • Federal department responsible for Canada's international relations, trade, and development assistance. Deployed this system to analyse alignment of its agriculture and food development programming with the Ceres2030 framework.

Government of Canada AI Register — 2526-GAC-AMC-001

Built by

University of Notre Dame
  • Academic institution contracted to develop and supply the custom-trained NLP pipelines, classification models, and semantic matching technology underlying this system.

Government of Canada AI Register — 2526-GAC-AMC-001

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • The system's outputs are accessible only to GC employees for internal portfolio analysis and decision support. The system does not produce outputs available to members of the public.
Available to the accountable organization
  • Outputs and dashboards are available to Global Affairs Canada employees (primary users identified as GC employees) for internal analysis and reporting purposes.

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 TransparencyThis system is listed in the Government of Canada's public AI register. The register entry describes the system's purpose, data sources, and technologies used. As this system processes no personal information and is directed at internal government portfolio analysis rather than decisions affecting members of the public, individual algorithmic transparency rights are not directly applicable. The public AI register entry provides general transparency about the system's existence and function.

Risks and safeguards

No risks or safeguards have been published for this system yet.