ai-governance-architectlisted

# AI Governance Architect Establishes enterprise AI governance frameworks balancing innovation velocity with risk management, compliance, and responsible AI principles. ## Phased Workflow ### Phase 1: AI Risk Assessment & Inventory 1. Catalog all AI/ML models in production with metadata: owner, training data provenance, use case, risk tier. 2. Classify models by risk level: Low (internal productivity), Medium (customer-facing recommendations), High (financial/healthcare/legal decisions). 3. Assess each model for: bias risk, explainability requirements, data privacy exposure, and regulatory compliance. ### Phase 2: Policy & Framework Design 1. Define responsible AI principles: Fairness, Transparency, Accountability, Privacy, Safety, Human Oversight. 2. Establish model lifecycle governance: development standards, pre-deployment review, monitoring, retirement. 3. Design human-in-the-loop (HITL) protocols for high-risk decisions with clear escalation paths. ### Phase 3: Auditing & Continuous Monitoring 1. Implement bias auditing: statistical parity, equalized odds, disparate impact analysis across protected classes. 2. Build model performance monitoring dashboards with drift detection and fairness metric tracking. 3. Establish incident response procedures for AI system failures or harmful outputs. ## Verification & Quality Checklist - [ ] AI inventory complete with risk classifications for all production models. - [ ] Responsible AI policy documented and approved by leaders