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

The Shift from Human to Model-Driven Decision-Making

• Transitioning from rule-based logic and human intuition to scored alternatives
• The velocity and volume of automated actions
• Emerging failure patterns and gaps in accountability

Inventory and Risk Stratification of Decisions

• Creating a catalog of automated choices
• Analyzing impact, reversibility, and affected stakeholders
• Defining risk tiers and corresponding control requirements
• Assigning ownership to each decision class

Accountability and Human Supervision

• Distinguishing between meaningful and nominal human review
• Treating autonomy thresholds as strategic governance decisions
• Defining escalation pathways and response latency requirements
• Designating accountable owners and approval mechanisms

Decision Documentation and Explainability

• Essential components of a defensible decision record
• Retention of confidence scores, options, and contextual data
• Traceability of model versions and lineage
• Communicating decision logic to clients or regulatory bodies

Policy and Boundary Management

• Managing approved option sets and implementing change controls
• Governing thresholds and conducting periodic reviews
• Identifying prohibited decisions and establishing hard stops
• Integrating policy-as-code with model outputs

Regulatory Obligations

• Compliance requirements under the EU AI Act for decision systems
• Industry-specific regulations in finance, insurance, and healthcare
• Data privacy laws and rights regarding automated decision-making
• Maintaining documentation and evidence of conformity

Risks Related to Vendors, Licensing, and Provenance

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

Monitoring for Bias, Fairness, and Outcomes

• Identifying outcome disparities across different groups
• Recognizing proxy variables and the framing of options
• Utilizing appeal and complaint signals as feedback
• Conducting periodic fairness assessments

Incident Management for Erroneous Decisions

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

Assurance Review of a Decision Layer

• Identifying evidence that auditors typically require
• Validating claims regarding accuracy and calibration
• Sampling decisions and reviewing overrides
• Reporting findings to the board of directors