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

Implications of Model-Driven Decisions

• Transitioning from rules and human judgment to scored options
• The speed and scale of automated actions
• Emerging failure modes and accountability gaps

Decision Inventory and Risk Classification

• Cataloging automated decisions
• Assessing impact, reversibility, and affected parties
• Defining risk tiers and control expectations
• Assigning ownership per decision class

Accountability and Human Oversight

• Distinguishing meaningful from nominal human review
• Treating autonomy thresholds as governance decisions
• Defining escalation routes and response times
• Identifying the accountable owner and sign-off process

Decision Records and Explainability

• Components of a defensible decision record
• Retention of confidence scores, options, and context
• Traceability of models and versions
• Explaining decisions to customers or regulators

Policy and Boundary Control

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

Regulatory Expectations

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

Vendor, License, and Provenance Risk

• Comparing hosted decision APIs versus open weights
• License terms for derivative and embedded models
• Assessing supplier dependency and discontinuation risks
• Ensuring weight integrity and version pinning

Bias, Fairness, and Outcome Monitoring

• Analyzing outcome disparities across groups
• Evaluating proxy variables and option framing
• Reviewing appeal and complaint signals
• Conducting periodic fairness reviews

Incident Response for Erroneous Decisions

• Detecting systematic decision errors
• Containing issues and suspending autonomy
• Implementing remediation and customer redress
• Analyzing root causes and updating controls

Assurance Review of a Decision Layer

• Identifying evidence auditors should request
• Testing accuracy and calibration claims
• Sampling decisions and overrides
• Reporting findings to the board