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

What Changes When Decisions Become Model-Driven

• From rules and human judgement to scored options
• Speed and scale of automated action
• New failure modes and accountability gaps

Decision Inventory and Risk Classification

• Cataloguing automated decisions
• Impact, reversibility and affected parties
• Risk tiers and control expectations
• Ownership per decision class

Accountability and Human Oversight

• Meaningful versus nominal human review
• Autonomy thresholds as governance decisions
• Escalation routes and response times
• Accountable owner and sign-off

Decision Records and Explainability

• What a defensible decision record contains
• Confidence, options and context retention
• Model and version traceability
• Explaining a decision to a customer or regulator

Policy and Boundary Control

• Approved option sets and change control
• Threshold governance and periodic review
• Prohibited decisions and hard stops
• Policy as code alongside model output

Regulatory Expectations

• EU AI Act obligations for decision systems
• Sector rules in finance, insurance and health
• Data protection and automated decision rights
• Documentation and conformity evidence

Vendor, Licence and Provenance Risk

• Hosted decision APIs versus open weights
• Licence terms for derivative and embedded models
• Supplier dependency and discontinuation risk
• Weight integrity and version pinning

Bias, Fairness and Outcome Monitoring

• Outcome disparity across groups
• Proxy variables and option framing
• Appeal and complaint signals
• Periodic fairness review

Incident Response for Wrong Decisions

• Detecting systematic decision error
• Containment and suspension of autonomy
• Remediation and customer redress
• Root cause and control update

Assurance Review of a Decision Layer

• Evidence an auditor should request
• Testing accuracy and calibration claims
• Sampling decisions and overrides
• Reporting findings to the board