Model Card — RetainIQ (v2.0, bias-mitigated)

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

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]

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.

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.

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.

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).

7. Quantitative Analyses

Sealed-test performance of the deployed v2.0 (age-only) champion, with the biased v1.0 for comparison:

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

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).

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.

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.

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.

Reference

Mitchell, M., et al. (2019). Model Cards for Model Reporting. FAT* '19. arXiv:1810.03993.