Draft a Protected-Leave Audit Memo: Spot AI-Assisted Layoff Bias Before Legal Does

The task

You're an HRBP or People Analytics lead. Your company just finalized (or is about to finalize) a reduction-in-force list — and any part of that list was scored, ranked, or filtered by an AI or algorithmic tool. Before the list goes out, you need a quick internal audit memo showing whether workers who took protected leave (FMLA, parental, disability accommodation, USERRA) are over-represented in the cut cohort, and hand that memo to Employment Counsel.

Before AI

Today this is a spreadsheet grind: pull the RIF cohort from Workday, join it to LOA history from your leave vendor, calculate selection rates for leave-takers vs. non-leave-takers, run a four-fifths-rule check, and write it up. Even for a mid-size RIF (300 in the pool, 60 selected), it's a 3-4 hour afternoon between the pivot tables and the memo draft — longer if your leave data lives in a separate system than your comp/perf data.

The lawsuit context is why this matters right now: a group of 26 Meta employees has sued the company, claiming it used artificial intelligence systems to select people for layoffs, disproportionately targeting those on medical, parental or family leave. Read the HR Dive breakdown of the complaint before you start — it shapes what your memo needs to address.

The workflow

Step 1 — Compute selection rates and a four-fifths-rule check

Paste your cohort table into the prompt below. One row per employee in the RIF-eligible population. Required columns: employee_id, selected_for_rif (Y/N), protected_leave_last_24mo (Y/N), leave_type, perf_rating, tenure_years, department, ai_score (the algorithmic ranking that fed the decision, if any).

You are a People Analytics lead preparing an internal audit for Employment Counsel on a pending reduction in force. Below is a CSV of the RIF-eligible cohort.

Do the following, showing your math:

1. Count total employees, total selected for RIF, and overall selection rate.
2. Split the population into two groups: (A) employees who took protected leave in the last 24 months, (B) employees who did not. Compute selection rate for each group.
3. Run the EEOC four-fifths rule test: is the selection rate for the non-leave group less than 80% of the leave group's selection rate, or vice versa? State the impact ratio clearly.
4. Break the leave group down by leave_type (FMLA medical, parental, disability accommodation, USERRA, other) and give selection rate for each subtype where n >= 5. Flag subtypes with n < 5 as "too small to analyze — do not report externally."
5. If an ai_score column is present, report the mean ai_score for the leave group vs. the non-leave group, and note whether the gap is >0.25 standard deviations.

Output a plain-text table plus a 3-bullet "signals" summary at the end. Do not draft a memo yet — just the numbers.

CSV follows below.

employee_id,selected_for_rif,protected_leave_last_24mo,leave_type,perf_rating,tenure_years,department,ai_score
E1001,N,N,none,3.8,4.2,Eng,72
E1002,Y,Y,parental,3.5,3.1,Eng,44
E1003,N,N,none,4.1,6.7,Eng,81
E1004,Y,Y,FMLA_medical,3.2,2.4,Eng,39
E1005,N,N,none,3.6,5.0,Eng,68
E1006,Y,N,none,2.9,1.8,Eng,41
E1007,N,Y,parental,3.9,7.2,Eng,74
E1008,Y,Y,FMLA_medical,3.4,3.6,Sales,45
E1009,N,N,none,3.7,4.1,Sales,70
E1010,N,N,none,4.0,8.3,Sales,79
E1011,Y,Y,disability_accom,3.3,2.9,Sales,42
E1012,N,N,none,3.8,5.5,Sales,71
E1013,Y,N,none,3.0,1.5,Sales,48
E1014,N,Y,parental,3.6,4.4,Sales,66
E1015,Y,Y,FMLA_medical,3.1,2.2,Ops,38
E1016,N,N,none,3.9,6.1,Ops,77
E1017,Y,Y,parental,3.4,3.3,Ops,46
E1018,N,N,none,3.7,4.8,Ops,69
E1019,Y,N,none,2.8,1.2,Ops,40
E1020,N,N,none,4.2,9.0,Ops,84
E1021,N,Y,USERRA,3.8,5.2,Ops,73
E1022,Y,Y,FMLA_medical,3.3,2.7,Marketing,43
E1023,N,N,none,3.6,4.6,Marketing,67
E1024,Y,Y,parental,3.2,2.1,Marketing,41
E1025,N,N,none,3.9,7.4,Marketing,76
E1026,Y,N,none,2.9,1.9,Marketing,47
E1027,N,Y,disability_accom,3.7,5.8,Marketing,70
E1028,Y,Y,parental,3.5,3.0,Eng,45
E1029,N,N,none,4.0,6.9,Eng,80
E1030,Y,Y,FMLA_medical,3.4,3.5,Eng,44

Step 2 — Stress-test the finding against confounders

Before anyone panics or dismisses the number, check whether the gap survives obvious controls (tenure, performance rating, department mix). This is the step that keeps counsel from getting blindsided.

Using the same cohort and the selection-rate numbers you just computed, do a confounder check.

1. Recompute leave-group vs. non-leave-group selection rates *within* each perf_rating band: <3.0, 3.0-3.4, 3.5-3.9, 4.0+. Note any band where the gap persists.
2. Recompute the same split within tenure bands: <2 years, 2-5, 5+. Note where the gap persists.
3. Recompute by department. Flag any department where leave-takers are >2x more likely to be selected.
4. If ai_score is present, compute: among employees with roughly matched perf_rating (within 0.3) and tenure (within 2 years), does the leave group still have a lower mean ai_score? Report the matched-pair gap.
5. State plainly: does the disparity look like it's driven by leave status itself, or is it explained by perf/tenure/department composition? If you can't tell from the data, say so.

Output: bulleted findings, no memo yet.

Step 3 — Draft the memo to Employment Counsel

Now turn the analysis into something a lawyer will actually read. Keep it short, factual, and privileged-looking.

Draft a 1-page internal memo from "People Analytics" to "Employment Counsel" titled "Pre-RIF Disparate Impact Screen — Protected Leave Cohort." Mark it "PRIVILEGED AND CONFIDENTIAL — PREPARED AT REQUEST OF COUNSEL" at the top.

Structure:
- Purpose (2 sentences): we ran a pre-notification screen on the pending RIF list for adverse impact on employees who took protected leave in the last 24 months.
- Method (3-4 bullets): population definition, leave definition, four-fifths test, confounder controls used.
- Findings (use the actual numbers from the prior steps — impact ratio, subgroup rates, whether the gap survives perf/tenure controls, ai_score gap if any).
- Small-cell caveat: any subgroup with n<5 was excluded from external reporting.
- Recommended next steps for counsel to consider: (a) hold notifications pending review of flagged individuals, (b) request the vendor's model card and training-data documentation for the AI scoring tool, (c) manual re-review of leave-group selections against a documented business-necessity rationale, (d) decide whether to loop in outside counsel before notifications go out.
- Do NOT include a legal conclusion. Do NOT say "we discriminated" or "we did not discriminate." Stick to observed rates.

Tone: dry, factual, no adjectives. No exclamation points. No "concerning" or "alarming" — just numbers.

Gotchas

• Leave data completeness. If your leave vendor only feeds HRIS on a monthly lag, your protected_leave_last_24mo flag will miss anyone whose leave started in the last 30-45 days — often the highest-risk group. Confirm the extract date with your benefits team before you trust the ratio.
• Small cells lie. With fewer than ~5 leave-takers in a subgroup, the four-fifths ratio swings wildly on one person. The prompt suppresses these, but re-read the output — LLMs sometimes report them anyway.
• AI score ≠ AI decision. If a manager saw the AI score but overrode it half the time, the disparity may live in the human layer, not the model. The confounder step won't catch that; you need decision-log data.
• This is not legal advice, and the memo is not privileged just because you labeled it that way. Privilege attaches when counsel actually directs the work. Get the request from Employment Counsel in writing before you run the audit, not after.
• The model will hallucinate percentages if the CSV is malformed. Spot-check at least one row of arithmetic by hand before forwarding.

Time saved

Manual version — pull, join, pivot, four-fifths calc, confounder splits, memo draft — is a solid 3-4 hours for a mid-size RIF cohort, longer if leave data lives outside your HRIS. This workflow compresses the analysis and the first memo draft into roughly 30-45 minutes, assuming your cohort CSV is already assembled. The bottleneck shifts from "doing the math" to "getting counsel's sign-off on the method" — which is where the time should be spent anyway.

Estimate, not a measurement. Your mileage depends on how clean the join between HRIS and your leave system is.

What we got when we ran it

We executed every prompt above, in order, against the sample input (model: claude-sonnet-4-6). Outputs below are real and unedited apart from length truncation.

Step 1 output

RIF Cohort Adverse Impact Audit — Numbers Only --- ### STEP 1 — Total Population, Selections, Overall Rate | Metric | Count | |---|---| | Total employees in cohort | 30 | | Total selected for RIF | 15 | | Total NOT selected | 15 | | **Overall selection rate** | **15 / 30 = 50.0%** | **Selected (Y):** E1002, E1004, E1006, E1008, E1011, E1013, E1015, E1017, E1019, E1022, E1024, E1026, E1028, E1030 — wait, let me recount carefully. Reading each row: | ID | Selected | Leave | |---|---|---| | E1001 | N | N | | E1002 | Y | Y | | E1003 | N | N | | E1004 | Y | Y | | E1005 | N | N | | E1006 | Y | N | | E1007 | N | Y | | E1008 | Y | Y | | E1009 | N | N | | E1010 | N | N | | E1011 | Y | Y | | E1012 | N | N | | E1013 | Y | N | | E1014 | N | Y | | E1015 | Y | Y | | E1016 | N | N | | E1017 | Y | Y | | E1018 | N | N | | E1019 | Y | N | | E1020 | N | N | | E1021 | N | Y | | E1022 | Y | Y | | E1023 | N

…output truncated at 250 words.

Step 2 output

Confounder Check — RIF Cohort --- ### Preliminary: Employee Classification Table For reference throughout, here is every employee with the variables needed: | ID | Selected | Leave | Leave Type | Perf | Tenure | Dept | ai_score | |---|---|---|---|---|---|---|---| | E1001 | N | N | none | 3.8 | 4.2 | Eng | 72 | | E1002 | Y | Y | parental | 3.5 | 3.1 | Eng | 44 | | E1003 | N | N | none | 4.1 | 6.7 | Eng | 81 | | E1004 | Y | Y | FMLA_medical | 3.2 | 2.4 | Eng | 39 | | E1005 | N | N | none | 3.6 | 5.0 | Eng | 68 | | E1006 | Y | N | none | 2.9 | 1.8 | Eng | 41 | | E1007 | N | Y | parental | 3.9 | 7.2 | Eng | 74 | | E1008 | Y | Y | FMLA_medical | 3.4 | 3.6 | Sales | 45 | | E1009 | N | N | none | 3.7 | 4.1 | Sales | 70 | | E1010 | N | N | none | 4.0 | 8.3 | Sales | 79 | | E1011 | Y | Y | disability_accom | 3.3 | 2.9 | Sales | 42 | | E1012 | N | N | none | 3.8 | 5.5 | Sales | 71 | | E1013 | Y

…output truncated at 250 words.

Step 3 output

PRIVILEGED AND CONFIDENTIAL — ATTORNEY-CLIENT COMMUNICATION PREPARED AT THE REQUEST OF COUNSEL — DO NOT DISTRIBUTE --- MEMORANDUM TO: Employment Counsel FROM: People Analytics DATE: [DATE] RE: Pre-RIF Disparate Impact Screen — Protected Leave Cohort --- PURPOSE People Analytics conducted a pre-notification adverse impact screen on the pending reduction in force list, focused on employees who took protected leave in the preceding 24 months. This memo transmits observed selection rates, confounder analysis results, and recommended next steps for counsel's review prior to any notifications being issued. --- METHOD - Population. The RIF-eligible cohort consists of 30 employees across four departments (Engineering, Sales, Operations, Marketing). Fourteen employees were designated for selection on the current RIF list. - Leave classification. Employees were coded as leave-group (Group A, n = 14) or non-leave group (Group B, n = 16) based on a binary protected-leave indicator covering the 24-month lookback period. Leave subtypes recorded include FMLA medical, parental, disability accommodation, USERRA, and other. Any subgroup with n < 5 was excluded from external-facing rate reporting. - Adverse impact test. Selection rates for Group A and Group B were computed and compared using the EEOC four-fifths (80%) rule. The impact ratio was calculated as the lower group's selection rate divided by the higher group's selection rate. - Confounder controls. Selection rates were recomputed within strata of performance rating (bands: below 3.0, 3.0–3.4, 3.5–3.9, 4.0 and above), tenure (bands: under 2 years, 2–5 years, 5 and above), and department. A partial matched-pair analysis was run on

…output truncated at 250 words.