enterprise-governance

name	enterprise-governance
description	This skill should be used when the user asks about "AI governance", "model fairness", "bias detection", "algorithmic bias", "disparate impact", "SHAP", "LIME", "model explainability", "interpretability", "XAI", "differential privacy", "DP-SGD", "federated learning", "adversarial robustness", "FGSM", "model compliance", "EU AI Act", "GDPR Article 22", "responsible AI", "model audit", "regulatory compliance", or when deploying AI in regulated industries (finance, healthcare, hiring, criminal justice).
version	1.0.0

Enterprise AI Governance — Fairness, Explainability, and Privacy

Provides comprehensive frameworks for responsible AI deployment in regulated enterprise environments. Covers algorithmic fairness analysis, SHAP/LIME explainability, adversarial robustness, differential privacy, and compliance with major AI regulatory frameworks.

Regulatory Context

Regulation	Requirement	Relevant Audit
EU AI Act (High-Risk)	Mandatory conformity assessment, human oversight	Full governance audit
GDPR Article 22	Right to explanation for automated decisions	SHAP/LIME local explanations
US ECOA/EEOC	No disparate impact in credit/employment decisions	Disparate Impact Ratio ≥ 0.8
HIPAA (Healthcare AI)	Privacy preservation in model training and inference	Differential privacy audit
Basel III/SR 11-7 (Finance)	Model validation and risk management	Bias, drift, and robustness audits

Algorithmic Fairness

Fairness Metrics (compute all for any regulated deployment):

• Demographic Parity: P(ŷ=1 | A=0) = P(ŷ=1 | A=1)
  • Violation: model assigns positive outcomes at different rates across groups
• Equal Opportunity: TPR_A=0 = TPR_A=1
  • Violation: model misses positive cases more often for one group
• Equalized Odds: both TPR and FPR equal across groups (stricter than Equal Opportunity)
• Disparate Impact Ratio: min_a P(ŷ=1|A=a) / max_a P(ŷ=1|A=a)
  • Threshold: ≥ 0.8 (EEOC 4/5ths rule) — below this triggers legal review

Bias remediation strategies:

• Pre-processing: reweight samples, resample minority group, relabel (Kamiran & Calders)
• In-processing: Lagrangian fairness constraint during training (Agarwal et al.)
• Post-processing: threshold calibration per demographic group (Hardt et al.)

See references/bias-detection.md for implementation, confidence intervals, and slice analysis methodology.

Explainability

SHAP (SHapley Additive exPlanations):

• Game-theoretic framework: φᵢ = contribution of feature i to prediction f(x)
• Unified property: Σᵢ φᵢ = f(x) − E[f(X)] (completeness)
• Efficient implementations:
  • TreeSHAP: exact, polynomial-time for tree models (XGBoost, LightGBM, Random Forest)
  • KernelSHAP: model-agnostic, samples coalitions for approximation (~200 samples)
  • DeepSHAP: backpropagation-based for neural networks

LIME (Local Interpretable Model-Agnostic Explanations):

• Perturbs input around point of interest; fits a local linear surrogate model g
• Local fidelity: g(x) ≈ f(x) in the neighborhood of x
• Use as sanity check against SHAP — major disagreements indicate model instability

See references/explainability.md for SHAP waterfall plots, global importance bar charts, and compliance report templates.

Adversarial Robustness

Fast Gradient Sign Method (FGSM): x_adv = x + ε · sign(∇_x L(θ, x, y))

Test at ε ∈ [0.01, 0.05, 0.1, 0.3] to construct an accuracy-vs-robustness curve.

Robustness thresholds:

• ε = 0.01 (imperceptible): accuracy should degrade < 5%
• ε = 0.05 (slight perturbation): accuracy should degrade < 20%
• ε = 0.1 (visible distortion): flag if accuracy < 50%

Adversarial training (remediation): augment training data with adversarial examples; retrains model on min_θ E[max_ε L(θ, x+δ, y)] (PGD training, Madry et al.)

Privacy-Preserving ML

Differential Privacy (DP-SGD):

• Add calibrated Gaussian noise to per-sample gradients before aggregation
• Privacy guarantee: (ε, δ)-DP — probability of inferring individual training record bounded
• Typical targets: ε ≤ 10 for "reasonable" privacy, ε ≤ 1 for strong privacy
• Cost: 5–20% accuracy reduction at strict DP settings; lower LR, smaller batch size

Federated Learning:

• Model trains locally on each client's data; only gradient updates (or model deltas) leave the device
• Aggregation: FedAvg — weighted average of client model updates
• Privacy amplification: combine with DP-SGD for strong privacy guarantees
• Communication efficiency: quantize gradients to 8-bit before upload

See references/privacy-preserving.md for full DP-SGD implementation, privacy budget accounting, and FL aggregation patterns.