TL;DR

AI governance defines policies, processes, and oversight for responsible AI use. Includes risk assessment, approval workflows, monitoring, and compliance with regulations.

Governance components

Policies: Acceptable use, data handling, model deployment standards
Processes: Approval workflows, risk assessment, review boards
Roles: AI ethics board, model owners, compliance officers
Documentation: Model cards, risk assessments, audit trails

Risk assessment framework

Classify AI systems by risk:

  • High-risk: Healthcare, hiring, credit decisions
  • Medium-risk: Customer service, recommendations
  • Low-risk: Internal tools, non-critical applications

Higher risk = stricter requirements:

  • Extensive testing
  • Human oversight
  • Regular audits
  • Explainability

Approval workflows

  1. Propose AI use case
  2. Risk assessment
  3. Ethics review
  4. Technical validation
  5. Legal/compliance check
  6. Approval or rejection
  7. Monitoring plan

Compliance considerations

  • GDPR (data protection, automated decisions)
  • EU AI Act (risk-based regulations)
  • Sector-specific (HIPAA, financial regulations)
  • Emerging AI regulations

Model inventory

Track all models in production:

  • Purpose and use cases
  • Training data provenance
  • Performance metrics
  • Responsible AI assessments
  • Owners and stakeholders

Continuous monitoring

  • Performance degradation
  • Bias drift
  • Compliance violations
  • Incident tracking

Best practices

  • Start governance early
  • Balance innovation and safety
  • Clear escalation paths
  • Regular training for teams
  • Transparent documentation