Check layoff selection decisions for fairness and adverse impact
Reviews proposed layoff selections against your criteria and flags inconsistencies and potential disparate impact before decisions are final.
The prompt
Help me review proposed layoff (reduction in force) selections before they are finalized.
Business reason for the reduction: [WHY and which roles/teams]
Selection criteria and weighting: [e.g. role elimination, skills needed going forward, performance ratings, tenure]
Pool of affected employees: [table with role, team, criteria scores, rating history, tenure, and - if available and permitted - age band, gender, and other protected characteristics]
Proposed selections: [WHO and why]
Location(s): [COUNTRY/STATE - affects notice and consultation rules]
Analyze:
1. Consistency - were the criteria applied the same way across the pool? Flag anyone selected or spared in a way the criteria don't explain.
2. Evidence quality - are scores backed by documentation, or based on recent or subjective impressions?
3. Adverse impact screen - compare selection rates by group (e.g. the four-fifths rule as a first screen) and flag groups selected at noticeably higher rates. Show the math.
4. Red flags - anyone recently on leave, who raised a complaint, or requested an accommodation.
5. Process requirements to check - notice periods, collective consultation thresholds, required disclosures [CONFIRM LOCAL LAW].
Output: a findings table (issue, who it affects, risk level, suggested action) and a short summary for leadership. Do not make final decisions; flag items for legal review. Never invent numbers - work only from the data I give you.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore HR & Hiring prompts
What to fill in
- WHY and which roles/teams
- WHO and why
- COUNTRY/STATE - affects notice and consultation rules
- CONFIRM LOCAL LAW
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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