| --- |
| license: mit |
| library_name: sklearn |
| pipeline_tag: tabular-classification |
| tags: |
| - tabular-classification |
| - hr-analytics |
| - fairness |
| - logistic-regression |
| - ai-governance |
| - model-card |
| datasets: |
| - REPLACE-WITH-YOUR-ORG/retainiq-data |
| --- |
| |
| # Model Card — RetainIQ (v2.0, bias-mitigated) |
|
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| *Employee-attrition risk model · Hireloom, Inc. · Structured per Mitchell et al. (2019), |
| "Model Cards for Model Reporting." Worked example for **The AI Governance Lab**. |
| Educational use only — not for real employment decisions.* |
|
|
| ## 1. Model Details |
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| | | | |
| |---|---| |
| | Model name | RetainIQ — Employee Attrition Risk Model | |
| | Owner | Hireloom, Inc. (provider **and** deployer — self-oversight / dogfooding) | |
| | Version / date | **v2.0 · June 2026** (supersedes v1.0 biased champion; see §8) | |
| | Model type | Binary classification — logistic regression (`C=0.1`, `class_weight=balanced`) | |
| | Bias mitigation | Fairlearn **CorrelationRemover on Age only** (the ADEA-protected attribute) | |
| | Pipeline | One-Hot + CorrelationRemover(Age) + StandardScaler → LogReg; 50 features; scikit-learn 1.7.2; seed 42 | |
| | Artifact | `RetainIQ_Champion_AgeOnly.pkl` (8,969 B) + readable coefficients (`_Inside.csv`) | |
| | Integrity (SHA-256) | `32a1a21bf9b4984eb5466f955bbef9a729a5535ca81ea279fb48a23513d74057` | |
| | Contact | [Hireloom AI Governance lead — placeholder] | |
|
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| ## 2. Intended Use |
| - **Primary use:** flag employees at elevated attrition risk so HR can prioritize retention outreach. **Decision-SUPPORT only**; monthly batch-scored, capacity-ranked list. |
| - **Intended users:** Hireloom HR / People team and people-managers. |
| - **Out of scope:** NOT for termination, discipline, compensation denial, or any adverse action; not autonomous; not validated outside Hireloom's workforce. |
|
|
| ## 3. Factors |
| Protected groups assessed: Age (ADEA-protected 40 line), Gender, Marital Status. Outputs a probability converted to a flag via a chosen threshold. |
|
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| ## 4. Metrics |
| ROC-AUC and PR-AUC (threshold-independent), plus recall, precision, F1 at 0.50. Because leavers are ~16%, PR-AUC and recall are prioritized over plain accuracy. The production operating threshold is a separate, capacity-driven choice set by HR. |
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| ## 5. Evaluation Data |
| Sealed test set: 294 employees (20% stratified hold-out, seed 42), 47 real leavers (16.0%). Quarantined from Module 5; opened once for the final grade. |
|
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| ## 6. Training Data |
| 1,176 employees (80% stratified split), 16.2% leavers. Protected attributes retained through preprocessing so fairness could be measured; the deployed pipeline then applies **age-only CorrelationRemover** (removes the Age signal; retains Gender/Marital Status). |
|
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| ## 7. Quantitative Analyses |
| Sealed-test performance of the deployed v2.0 (age-only) champion, with the biased v1.0 for comparison: |
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| | Metric | Deployed v2.0 (age-only) | Biased v1.0 (rejected) | |
| |---|---|---| |
| | ROC-AUC | 0.811 | 0.812 | |
| | PR-AUC | 0.534 | 0.588 | |
| | Recall | 0.702 | 0.681 | |
| | Precision | 0.407 | 0.381 | |
| | F1 | 0.516 | 0.489 | |
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| **Held-out fairness (equalized-odds difference; lower = fairer):** Age 40+ **0.045** (was 0.369 — gap closed; 40+ recall 0.44→0.69); Marital Status 0.281 (improved); Gender 0.026 (held fair). *Caveat:* PR-AUC is marginally lower (0.534), so the gain is clearest at the chosen operating point; per-group counts are small (40+ n=16, Divorced n=4). |
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| **Why age-only:** a three-way bake-off showed all-attribute CorrelationRemover *worsened* gender (EOD 0.033→0.137) and cost more accuracy, while age-only closed the ADEA gap with no gender regression and no accuracy cost. |
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| ## 8. Ethical Considerations |
| - **Deployment decision (R-16) — RESOLVED:** deploy the **age-only bias-mitigated champion (v2.0)**, prioritizing the ADEA-protected age group (largest gap + greatest legal exposure). The biased v1.0 and the all-3-attribute variant are documented as considered-and-rejected alternatives. |
| - **Residual / monitoring targets:** the marital-status spread persists, and gender balance must be watched to confirm the untargeted groups don't drift — both are named monitoring targets (Module 9). |
| - **Self-oversight / dogfooding:** Hireloom is both provider and deployer; independent fairness review recommended. |
| - **Misuse risk:** flags are decision-support, not verdicts; adverse use would be inappropriate and likely unlawful. |
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| ## 9. Caveats & Recommendations |
| - Synthetic data stand-in: revalidate on real Hireloom data before any deployment. |
| - Threshold: set/document the production operating point by HR capacity; re-run the fairness audit AT that threshold per protected group. |
| - Monitoring: watch drift, the gender EOD, and the marital spread; define retrain/retire triggers. |
| - Legal/compliance (not legal advice): obtain review of ADEA, Title VII, NYC Local Law 144, EEOC guidance, EU AI Act (high-risk employment) and GDPR special-category obligations before real deployment. |
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| ## Reference |
| Mitchell, M., et al. (2019). *Model Cards for Model Reporting.* FAT* '19. arXiv:1810.03993. |
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