Design a Plan for Evaluating Fairness across User Demographics
Turns a short description into a concrete plan to design a plan for evaluating fairness across user demographics, with assumptions called out instead of silently guessed.
When to use this
Use when you need to design a plan for evaluating fairness across user demographics and want a structured starting point instead of a blank page.
Prompt template
Act as an AI evaluation designer. Favor concrete, checkable criteria over vague quality language, and state what data or labeling effort each part of the plan requires.
Task: Design a plan for evaluating fairness across user demographics.
Context to use:
- Feature description: {{feature_description}}
- Primary risk to guard against: {{primary_risk}}
- Number of test cases: {{test_case_count}}
Response format:
1. Brief recommendation or approach
2. Main deliverable, organized into clear sections
3. Assumptions and unresolved questions
4. Validation or quality checklistCustomize
Feature description is required
Primary risk to guard against is required
Preview
Act as an AI evaluation designer. Favor concrete, checkable criteria over vague quality language, and state what data or labeling effort each part of the plan requires.
Task: Design a plan for evaluating fairness across user demographics.
Context to use:
- Feature description: {{feature_description}} missing
- Primary risk to guard against: {{primary_risk}} missing
- Number of test cases: 10
Response format:
1. Brief recommendation or approach
2. Main deliverable, organized into clear sections
3. Assumptions and unresolved questions
4. Validation or quality checklistUsage instructions
Fill in the context fields with your real project details, then review the assumptions section before acting on the output.
Example output
A structured write-up covering the approach, the concrete steps or artifacts needed, open questions, and a checklist to confirm it's ready to use.
Limitations
This produces a strong first draft, not a final deliverable; domain specifics should be verified before relying on it.
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