Fabizi

AI Governance

AI Governance Frameworks That Actually Get Used

December 1, 2024

6 min read

Why Most AI Governance Fails

Typical Governance Document:

  • 47 pages of principles and definitions
  • Vague requirements like "ensure fairness"
  • No concrete decision criteria
  • Reviewed quarterly by committee

What Happens:

  • Engineers ignore it
  • Projects proceed without governance review
  • Risk accumulates silently
  • Framework updated annually, never enforced

The Three Questions Governance Must Answer

  1. What AI use cases are allowed, prohibited, or require review?

    • Clear categorization by risk level
    • Concrete examples, not abstract principles
    • Decision tree that works without legal consultation

  2. What technical requirements apply to each category?

    • Testing requirements
    • Documentation standards
    • Monitoring and audit trails
    • Human oversight specifications

  3. Who makes decisions and how quickly?

    • Clear approval authority by risk level
    • Defined SLAs for governance review
    • Escalation paths

Risk-Based Classification That Works

Tier 1: Prohibited Use Cases

Definition: AI systems with unacceptable risk that are banned regardless of controls.

Examples:

  • Social credit scoring systems
  • Emotion recognition in hiring decisions
  • Predictive policing without human review
  • Subliminal manipulation techniques

Governance Decision: Automatic rejection. No approval process needed.

Tier 2: High-Risk Use Cases (Requires Review)

Definition: AI systems that could cause significant harm and require extensive controls.

Examples:

  • Credit decisioning systems
  • Healthcare diagnostic support
  • Employment screening algorithms
  • Critical infrastructure control systems

Required Controls:

  • Human-in-the-loop for final decisions
  • Bias testing and fairness metrics
  • Explainability requirements
  • Continuous monitoring
  • Incident response plan
  • Regular audits

Approval Process: Governance board review, 2-week SLA, documented risk assessment required.

Tier 3: Medium-Risk Use Cases (Self-Assessment)

Definition: AI systems with limited scope and well-understood risks.

Examples:

  • Content recommendation systems
  • Inventory optimization
  • Email spam filtering
  • Document classification

Required Controls:

  • Standard testing procedures
  • Monitoring for drift
  • Incident logging
  • Quarterly reviews

Approval Process: Team lead approval with checklist, 48-hour SLA.

Tier 4: Low-Risk Use Cases (No Review)

Definition: AI systems with minimal potential for harm.

Examples:

  • Image enhancement tools
  • Grammar checking
  • Video game AI opponents
  • Personal productivity assistants

Required Controls:

  • Basic testing
  • User feedback mechanisms

Approval Process: None. Standard engineering practices apply.

Technical Requirements by Tier

Data Governance (Tier 2+)

Data Source Documentation:

  • Where did training data come from?
  • What biases might it contain?
  • How was it validated?

Data Minimization:

  • Only collect data necessary for function
  • Define retention policies
  • Enable data deletion

Privacy Controls:

  • PII detection and handling
  • Differential privacy where applicable
  • Consent mechanisms

Model Governance (Tier 2+)

Testing Requirements:

  • Performance metrics on test data
  • Fairness metrics across demographic groups
  • Adversarial robustness testing
  • Edge case analysis

Documentation:

  • Model architecture and parameters
  • Training methodology
  • Performance limitations
  • Known failure modes

Explainability:

  • Model interpretation capabilities
  • Feature importance analysis
  • Decision justification for individual predictions

Monitoring and Auditing (Tier 2+)

Production Monitoring:

  • Model performance drift detection
  • Input distribution changes
  • Prediction confidence tracking
  • Error rate monitoring

Audit Trail:

  • All predictions logged with context
  • Model version tracking
  • Configuration changes recorded
  • Access controls and logs

Incident Response:

  • Definition of AI incidents
  • Response procedures
  • Escalation criteria
  • Post-incident review process

Governance Workflow That Developers Accept

Step 1: Self-Assessment (5 minutes)

Engineer completes simple questionnaire:

  • What does the AI system do?
  • What decisions does it make?
  • Who is affected?
  • What data does it use?

Output: Risk tier assignment with explanation.

Step 2: Requirements Checklist (Tier-Dependent)

Tier 4: No additional requirements.

Tier 3: Complete standard testing checklist, get team lead approval.

Tier 2: Complete comprehensive documentation template, schedule governance review.

Tier 1: Project rejected with explanation and alternatives.

Step 3: Review (Tier 2 Only)

2-Week SLA for Initial Review:

  • Risk assessment validation
  • Control adequacy evaluation
  • Approval, conditional approval, or rejection

No SLA Creep:

  • Timer starts when complete documentation submitted
  • Incomplete submissions returned immediately
  • Reviews don't block on committee availability

Step 4: Ongoing Monitoring (All Tiers)

Automated Dashboards:

  • Model performance metrics
  • Drift detection alerts
  • Incident tracking

Scheduled Reviews:

  • Tier 2: Quarterly
  • Tier 3: Annual
  • Tier 4: None

Making It Stick: Organizational Integration

Developer Experience

Good Governance:

  • CLI tool for risk assessment
  • Automated checklist validation
  • Template generation
  • Clear approval status

Bad Governance:

  • Email attachments and Word documents
  • Unclear submission requirements
  • Black-box review process
  • No status visibility

Incentive Alignment

Make Governance Helpful:

  • Risk assessment catches issues early
  • Templates save documentation time
  • Checklist prevents deployment problems
  • Review provides expert feedback

Avoid Punitive Framing:

  • Not: "Governance prevents bad AI"
  • Instead: "Governance helps ship safer AI faster"

Executive Support

Governance Without Authority Fails:

  • Executive sponsorship required
  • Clear consequences for circumventing process
  • Resources allocated for governance function
  • Regular reporting to leadership

Common Implementation Mistakes

Mistake 1: Starting Too Comprehensive

What Happens:

  • Year-long framework development
  • Covers every edge case
  • Never gets adopted

Instead:

  • Start with Tier 2 high-risk systems only
  • Simple three-tier classification
  • Expand based on actual usage

Mistake 2: Treating All AI the Same

What Happens:

  • Spell-checker requires same review as credit scoring
  • Engineers bypass governance entirely
  • High-risk systems escape oversight

Instead:

  • Risk-based tiers with different requirements
  • Focus governance resources on high-risk

Mistake 3: Governance as Compliance Theater

What Happens:

  • Checkboxes get checked
  • No real risk reduction
  • False sense of security

Instead:

  • Technical controls that actually mitigate risk
  • Automated validation where possible
  • Meaningful review by qualified people

Key Takeaways

  • Risk-based classification with concrete examples beats abstract principles
  • Fast approval for low-risk, thorough review for high-risk
  • Technical requirements must be specific and measurable
  • Developer experience determines adoption
  • Governance that helps teams ship safer systems gets used

Start with a simple three-tier system, focus on high-risk use cases, and expand based on what you learn. The best governance framework is the one engineering teams actually follow.

Related services: AI governance frameworks, regulatory compliance strategy