AI Governance: Frameworks for Responsible Enterprise Adoption

AI Without Governance Is a Liability, Not an Asset

As organizations deploy more models in production, risks accumulate: models that discriminate undetected, decisions that can't be explained to regulators, model versions without traceability, and nobody knows how many models are in production or who is responsible for each one. AI governance is not bureaucracy: it is the infrastructure that enables scaling AI safely.

The Three Lines of Defense Applied to AI

Fairness Metrics and Bias Testing

Fairness is not a single metric: it is a set of criteria that can be mutually exclusive. Demographic parity (same approval rate across groups), equalized odds (same TPR and FPR across groups), and predictive parity (same PPV across groups) cannot be simultaneously satisfied except in trivial cases (Chouldechova-Kleinberg theorem). The organization must choose which criterion to prioritize based on context: in credit, equalized odds is preferred; in hiring, demographic parity may be regulatory required.

Model Inventory and Lifecycle Management

A model inventory is the centralized catalog of all production models, with metadata: purpose, owner, version, deploy date, performance metrics, training data, risk tier, last validation date, and next scheduled review. Without an inventory, the organization doesn't know what models it has, which are degraded, or which have been validated. The inventory feeds the governance cycle: high-risk models (Tier 1) require independent validation before deploy; low-risk models (Tier 3) only require automated monitoring.