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

Shifting Paradigms in Model-Driven Decision Making

• Transitioning from static 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 decision points
• Analyzing impact, reversibility, and affected stakeholders
• Defining risk tiers and corresponding control expectations
• Assigning ownership for each decision class

Accountability and Human Oversight

• Distinguishing meaningful human review from nominal oversight
• Treating autonomy thresholds as governance decisions
• Defining escalation routes and response time standards
• Identifying accountable owners and sign-off authorities

Decision Records and Explainability

• Components of a defensible decision record
• Retaining confidence levels, options, and contextual data
• Ensuring model and version traceability
• Explaining decisions to customers or regulators

Policy and Boundary Control

• 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 Expectations

• Obligations under the EU AI Act for decision systems
• Sector-specific regulations in finance, insurance, and healthcare
• Data protection laws and rights regarding automated decisions
• Requirements for documentation and conformity evidence

Vendor, License, and Provenance Risk

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

Bias, Fairness, and Outcome Monitoring

• Detecting outcome disparities across different groups
• Identifying proxy variables and option framing issues
• Utilizing appeal and complaint signals for monitoring
• Conducting periodic fairness reviews

Incident Response for Incorrect Decisions

• Detecting systematic decision errors
• Containment strategies and suspending autonomy
• Remediation processes and customer redress mechanisms
• Root cause analysis and control updates

Assurance Review of Decision Layers

• Evidence requirements for auditors
• Testing accuracy claims and calibration metrics
• Sampling decisions and human overrides
• Reporting findings to the board of directors