Deliverable 4b · Ethics & Integrity Controls
Ethics & Explainability
Bias mitigation, model explainability, and fairness assessment — operationalized as testable procedures and wired into release gates, not stated as principles. Aligned to NIST AI RMF MEASURE and the GAO Performance principle.
Bias mitigation
Pre-, in-, post-processing and in-production controls with a defined fairness schedule.
Explainability
SHAP / LIME attributions on every high-stakes decision, paired with audit trails.
Fairness assessment
Disparate-impact testing against the 4/5ths rule with documented procedures.
Fairness assessment · live
Hotline Complaint Prioritization
Selection rate parity (80% rule)
| Subgroup | Selection | Parity | n |
|---|---|---|---|
| Region A | 61% | 1.00 | 18,420 |
| Region B | 57% | 0.93 | 14,870 |
| Region C | 53% | 0.87 | 9,340 |
| Region D | 50% | 0.82 | 6,810 |
| Region E | 49% | 0.80 | 4,220 |
After applying class-weight rebalancing and re-calibrating the regional complaint-volume normalization factor in Q4 FY2025, all five regional subgroups now meet or exceed the 4/5ths (0.80) parity threshold. Region E, previously the lowest-performing subgroup at 0.74 pre-mitigation, reached exactly 0.80 post-mitigation and will be monitored monthly for regression.
Explainability · SHAP
Contractor Fraud Risk Score
Feature contributions for a single prediction — red pushes risk up, green pulls it down.
Top drivers (plain language)
- 1 Invoice totals exceeded the contract ceiling by 31% across the last three billing periods, which is the single strongest predictor of inflated or fabricated charges in this model.
- 2 Fourteen change orders were submitted in 90 days — more than four times the median for comparable contracts — suggesting scope-creep manipulation or unauthorized work authorization.
- 3 A single subcontractor accounted for 89% of subcontracted spend, raising concerns about pass-through billing arrangements and lack of competitive sourcing.
Required human sign-off
Reviewed by SA-4412 on 2026-06-02. SHAP output consistent with independent document review. Elevated to full investigation per SOP-INV-07. Case ID: OIG-2026-CF-0883.
Bias-mitigation pipeline
Resampling, reweighting, and representation augmentation so the training data reflects the population.
Fairness constraints in training — demographic parity, equalized odds, calibration across subgroups.
Threshold adjustment and reject-option review where disparities persist after training.
Continuous disparate-impact testing with the 4/5ths rule and monthly regression checks.
Release-gate checkpoints
No model reaches production until every gate is green. These checkpoints are wired into the AI release pipeline.
- Bias & disparate-impact audit passes the 4/5ths rule across all defined subgroups
- Explainability (SHAP/LIME) available for every high-stakes prediction
- Model card documents data, intended use, limitations, and TLP classification
- Human-oversight model defined and override logging configured
- Adversarial red-team evaluation complete for High-Impact systems
- OIG Counsel sign-off for any protected-characteristic proxy variable