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AI-Assisted Quality Assurance and Chatbot Platform for Research Staff

Research & Development · Employment & Work

What it collects that can identify you

About behaviour
Pseudonymous data
  • Staff queries and prompts submitted to the chatbot, which may include information about the user's information needs and workflows.

Also collects operational data, which is anonymized data.

Run by
Natural Sciences and Engineering Research Council of Canada (NSERC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization, Vendor

What it is for

Research & DevelopmentEmployment & Work

This system uses Microsoft Azure AI Foundry to help Government of Canada employees at NSERC automate software testing and build internal chatbots that answer questions using organizational data. It is currently in development and is intended for internal staff use only. The most important transparency point is that responses from the chatbots are generated by AI and may not always be accurate — staff should verify important information.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Organizational data (policies, procedures, internal documentation) used by the chatbot to generate responses. Also software test cases, code, and related QA artifacts used for test automation.
About behaviour
Pseudonymous data
  • Staff queries and prompts submitted to the chatbot, which may include information about the user's information needs and workflows.

Processing

Language Models
  • Azure AI Foundry provides access to large language models (LLMs) used for chatbot response generation and test case creation. Specific model versions are not disclosed in the register.
Classification & Prediction
  • The QA automation component classifies test results (pass/fail) and validates test cases, applying classification logic to software outputs.

What it does

Deciding (Analytical AI)
Human decides
  • The AI generates, executes, and validates test cases to support QA staff decisions about software quality. Test results are reviewed by human staff before action is taken.
Creating (Generative AI)
Human decides
  • The platform generates responses to staff queries through customized chatbots that leverage organizational data, producing tailored text answers. Responses are advisory and staff retain judgment.
Understanding (Semantic AI)
Human decides
  • The chatbot component retrieves and understands meaning from organizational data to deliver tailored, policy-compliant responses to staff questions.

Outputs

Generated content
Anonymized data
  • AI-generated chatbot responses to staff queries, drawing on organizational data. Also AI-generated test cases and test scripts for software QA.
Operational data
Anonymized data
  • Test validation reports, pass/fail results, and QA metrics produced by the automated testing pipeline for staff review.

Run by

Natural Sciences and Engineering Research Council of Canada (NSERC)
  • NSERC is the federal department deploying Azure AI Foundry for internal quality assurance and chatbot use cases among its Government of Canada employees.

Government of Canada AI Register — 2526-NSERC-CRSNG-003

Built by

Microsoft
  • Microsoft provides the Azure AI Foundry platform, which supplies the underlying AI models and infrastructure used by NSERC for test automation and chatbot development.

Government of Canada AI Register — 2526-NSERC-CRSNG-003

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs (chatbot responses, test results) are available to NSERC GC employees using the internal systems. Data is not shared publicly.
Available to vendor
  • Microsoft, as the platform vendor, may have access to data processed through Azure AI Foundry in accordance with cloud service agreements. The specific scope of vendor data access is not detailed in the register.

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 TransparencyGC employees are entitled to understand that responses are AI-generated and that the system leverages organizational data via Azure AI Foundry. The existence of this system is disclosed on the Government of Canada AI Register. Staff should be informed when interacting with an AI-powered chatbot rather than a human colleague.
  • Right to Be Informed of AI UseStaff using the chatbot should be informed that they are interacting with an AI system, not a human. This right is especially relevant given the chatbot is designed to mimic information assistance and deliver tailored responses using organizational data.

Risks and safeguards

  • Societal & cultural harmAI-generated chatbot responses may spread inaccurate information if staff rely on them uncritically, degrading institutional knowledge quality.Safeguard: The system is designed to deliver policy-compliant responses; staff are expected to verify important outputs. The system is currently in development, allowing for review before wider rollout.