Governance of Automated Decisions: Audit, Oversight and Control for AI Decision Models Training Course

Shifting to Model-Driven Decisions

• The transition from static rules and human judgement to scored options
• The increased speed and scale of automated actions
• Emerging failure modes and accountability gaps

Decision Inventory and Risk Categorization

• Compiling an inventory of automated decisions
• Assessing impact, reversibility, and affected stakeholders
• Defining risk tiers and corresponding control expectations
• Assigning ownership for each decision category

Accountability and Human Oversight

• Distinguishing between meaningful and nominal human review
• Treating autonomy thresholds as governance decisions
• Defining escalation pathways and response time requirements
• Identifying accountable owners and sign-off responsibilities

Decision Records and Explainability

• Essential components of a defensible decision record
• Retention of confidence scores, alternatives, and contextual data
• Ensuring model and version traceability
• Communicating decisions to customers or regulatory bodies

Policy and Boundary Management

• Managing approved option sets and implementing change control
• Governing thresholds and conducting periodic reviews
• Establishing prohibited decisions and hard stops
• Implementing policy as code alongside model outputs

Regulatory Requirements

• Compliance obligations under the EU AI Act for decision systems
• Sector-specific regulations in finance, insurance, and healthcare
• Data protection standards and rights regarding automated decisions
• Documentation requirements and evidence of conformity

Vendor, Licensing, and Provenance Risks

• Comparing hosted decision APIs with open-weight models
• Reviewing licensing terms for derivative and embedded models
• Assessing supplier dependency and risks of discontinuation
• Ensuring weight integrity and version pinning

Bias, Fairness, and Outcome Monitoring

• Detecting outcome disparities across different groups
• Identifying proxy variables and biases in option framing
• Monitoring appeal and complaint signals
• Conducting periodic fairness assessments

Incident Response for Erroneous Decisions

• Detecting systematic errors in decision-making
• Containing issues and suspending autonomy when necessary
• Executing remediation plans and providing customer redress
• Performing root cause analysis and updating controls

Assurance Review of Decision Layers

• Identifying evidence required for auditor review
• Validating accuracy and calibration claims through testing
• Sampling decisions and reviewing manual overrides
• Reporting findings to the board of directors