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AI-Assisted Data Entry for Grants and Contributions

Eligibility & Public Benefits · Planning & Decision-making

What it collects

Operational data
Anonymized data
  • Project documentation for grants and contributions to international assistance programs — including project descriptions, objectives, and related administrative records — used as input for AI analysis and data coding.
Run by
Global Affairs Canada (GAC)
Where
No fixed location
Kept
Retained Not specified in register entry
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Eligibility & Public BenefitsPlanning & Decision-making

This system uses artificial intelligence to assist Government of Canada employees at Global Affairs Canada in entering and coding data for grants and contributions to international assistance programs. It extracts information from project documents, classifies projects by standard aid codes, and helps automate data entry to improve accuracy and consistency. The system does not process personal information and its use of AI has been disclosed to users.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Project documentation for grants and contributions to international assistance programs — including project descriptions, objectives, and related administrative records — used as input for AI analysis and data coding.

Processing

Language Models
  • LLMs deployed in Azure ML Studio convert extracted document text into structured JSON and support bilingual (English/French) processing. An NLP and LLM ensemble classifies projects by DAC codes, policy markers, and countries.
Classification & Prediction
  • An NLP and LLM ensemble classifies international assistance projects by DAC codes, policy markers, and countries. The model is optimized and fine-tuned to assess and attain accuracy targets.

What it does

Sensing (Perceptive AI)
Human decides
  • Azure AI Document Intelligence and AI Vision extract structured text from grant documents, converting unstructured document content into machine-readable fields for downstream processing.
Understanding (Semantic AI)
Human decides
  • LLMs in Azure ML Studio convert extracted document content into JSON for system ingestion, and an NLP and LLM ensemble classifies projects by DAC codes, policy markers, and countries.
Deciding (Analytical AI)
Human decides
  • The NLP and LLM ensemble classifies projects by DAC codes, policy markers, and countries, and the model is optimized and fine-tuned to assess and attain coding accuracy.

Outputs

Operational data
Anonymized data
  • AI-generated project coding (DAC codes, policy markers, country classifications) and structured JSON records ready for ingestion into grants and contributions management systems. Robotic process automation for data entry automation is noted as upcoming.
A recommendation or prediction
Anonymized data
  • The system generates suggested project codes and classifications (DAC codes, policy markers, countries) that GC employees review and confirm before entry into management systems — the AI advises rather than determines.

Run by

Global Affairs Canada (GAC)
  • Global Affairs Canada is the federal department accountable for deploying and operating this AI-assisted data entry system for grants and contributions to international assistance programs.

GC AI Register — 2526-GAC-AMC-004

Built by

Deloitte
  • Deloitte is the vendor that developed and supplied the AI-assisted data entry solution for Global Affairs Canada, using Microsoft Azure AI services and custom language models.

GC AI Register — 2526-GAC-AMC-004

Kept for

Retained Not specified in register entry
  • The register entry does not specify a retention period. Outputs (coded project records) are likely subject to Government of Canada information management and records retention policies for grants and contributions data.
  • Duration: Not specified in register entry

Shared with

Available to the accountable organization
  • Output data (project codes, structured records) is available to Global Affairs Canada employees who use it to populate grants and contributions management systems.
Not available to me
  • This system processes administrative project documentation and does not involve personal information. Members of the public are not data subjects and have no applicable individual data access.

Stored

Stored on 3rd Party Cloud
  • The solution uses Microsoft Azure services (Azure AI Document Intelligence, AI Vision, Azure ML Studio), indicating data is processed and likely stored on Microsoft Azure cloud infrastructure.
  • Duration: Not specified in register entry
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 TransparencyAI use has been disclosed to users (GC employees). The register entry is publicly available on the Government of Canada's open data portal, describing the AI models and techniques used.
  • Right to Be Informed of AI UseAI use is disclosed to GC employees who are the primary users of this system. The register entry states that AI use has been disclosed to users.

Risks and safeguards

  • Reputational harmInaccurate AI-generated project codes (DAC codes, policy markers) could misclassify international assistance projects, potentially affecting Canada's international reporting and accountability.Safeguard: The system includes optimization and fine-tuning to assess and attain accuracy, and GC employees review AI-generated suggestions before final entry. Tools and processes for monitoring and maintenance are also in place.
  • Societal & cultural harmSystematic miscoding of international assistance projects could distort reporting on Canada's development aid commitments and affect downstream accountability and planning decisions.Safeguard: The system supports bilingual processing (English and French), human review by GC employees is the final step, and monitoring and maintenance tools are in place. The register notes this is expected to lead to better investment planning, prioritizing, accountability, monitoring and reporting.