Decision inputs
Facts that change the policy answer
Here the tool receives test cases, model outputs and acceptance criteria, while someone ultimately relies on an evaluation summary and failure catalogue. The policy must evaluate the whole path between them.
- 1Task and owner
- Machine learning evaluator wants to evaluate an AI model. The request needs an accountable owner for an evaluation summary and failure catalogue, even when the tool prepares most of the first draft.
- 2Information involved
- Test cases, model outputs and acceptance criteria. Look beyond pasted text: files, integrations and retrieval connections can expose the same material.
- 3Tool and account
- An approved company account. Approval must cover the account and its settings, not merely the product name.
- 4Intended result
- The expected result is an evaluation summary and failure catalogue. Its destination matters: private working material creates a different consequence from a sent, published or automated result.
- 5Consequence if it is wrong
- A narrow test set can hide important failures or overstate readiness. That risk sets the level of review and the person who should receive an exception.
- 6Human review
- model and risk owners should inspect, change, reject or stop the result. Make the review happen before reliance and give the reviewer a real way to stop the work.
Possible policy routes
The task name alone cannot decide it.
A published workplace policy can return different answers for the same task. These are the practical branches worth encoding.
A routine policy route may be possible
The lower-friction route begins when the exact account is approved, only the minimum model evaluation data is used, an evaluation summary and failure catalogue remains within the stated purpose, and model and risk owners reviews it before use.
Approval may be required
The request moves beyond routine handling when the account or data handling is uncertain, a narrow test set can hide important failures or overstate readiness, or an evaluation summary and failure catalogue reaches people or systems beyond the requester’s authority.
The request may need to stop or change
The policy may require another method where restricted information would enter an unapproved service, the output would act before model and risk owners can intervene, or include edge cases, affected groups and reproducible evaluation data cannot be maintained. Consider less information, a controlled account or a non-AI process.
Request checklist
Questions to ask before using the tool
- 01
Does the selected account retain or reuse anything supplied while trying to evaluate an AI model?
- 02
Does the proposed input include more of test cases, model outputs and acceptance criteria than the result actually requires?
- 03
Does an evaluation summary and failure catalogue create an external statement, a decision or an automated action?
- 04
Will model and risk owners review before the result is sent, published or acted upon?
- 05
Which change in tool, data, purpose or impact would require a fresh request?
Worked request
What the employee should submit
This example supplies decision facts without pasting the underlying material into the approval record.
- requester
- machine learning evaluator
- task
- Use AI to evaluate an AI model.
- information
- test cases, model outputs and acceptance criteria
- tool
- An approved company account
- frequency
- Recurring work
- region
- Where the work and affected people are located
- purpose
- Analyse
- impact
- Model release decision
- review
- Complete human review
- owner
- model and risk owners
Useful safeguards
Controls that fit this request
- ✓
Include edge cases, affected groups and reproducible evaluation data
- ✓
Reduce test cases, model outputs and acceptance criteria to the smallest useful extract and remove fields unrelated to an evaluation summary and failure catalogue.
- ✓
Set an expiry or review point when recurring work turns into a permanent process.
- ✓
Keep the submitted facts, model and risk owners’s decision and the exact published policy version.
Questions people ask
About this AI use
Is using AI to evaluate an AI model automatically allowed?
The task name cannot settle the answer. Apply the company’s published rules to test cases, model outputs and acceptance criteria, the exact account, an evaluation summary and failure catalogue, its audience and the proposed review.
How specific should the workplace AI request be?
Describe an evaluation summary and failure catalogue, identify test cases, model outputs and acceptance criteria, name the exact tool and account, explain who will receive or rely on the output, and state how model and risk owners will review it.
What should remain after the decision?
Keep the submitted facts, model and risk owners’s decision and the exact published policy version. A classification and controlled reference may be enough when copying test cases, model outputs and acceptance criteria would create unnecessary risk.