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

Shifting to Model-Driven Decision Making

• Transitioning from rigid rules and human intuition to scored alternatives
• The pace and volume of automated actions
• Emerging failure patterns and accountability voids

Inventorying Decisions and Classifying Risk

• Maintaining a registry of automated decisions
• Evaluating impact, reversibility, and stakeholder effects
• Defining risk tiers and corresponding control standards
• Assigning ownership for each decision class

Accountability and Human Supervision

• Distinguishing between meaningful and tokenistic human review
• Treating autonomy thresholds as key governance choices
• Defining escalation paths and response timelines
• Identifying responsible owners and approval processes

Decision Logging and Interpretability

• Components of a defensible decision record
• Preserving confidence scores, options, and contextual data
• Tracking model lineage and version history
• Communicating decision logic to customers and regulators

Managing Policy and Boundaries

• Governing approved option sets and change management
• Overseeing threshold adjustments and periodic reviews
• Establishing prohibited actions and mandatory stop rules
• Implementing policy-as-code alongside model outputs

Regulatory Requirements

• EU AI Act requirements for decision-making systems
• Industry-specific regulations in finance, insurance, and healthcare
• Data privacy laws and rights regarding automated decisions
• Documentation and evidence of regulatory compliance

Vendor, Licensing, and Provenance Risks

• Comparing hosted decision APIs with open-weight models
• Reviewing license agreements for derived and embedded models
• Assessing supplier reliance and discontinuation risks
• Ensuring weight integrity and version locking

Bias, Equity, and Outcome Tracking

• Analyzing disparities in outcomes across different groups
• Identifying proxy variables and framing of options
• Monitoring signals from appeals and complaints
• Conducting regular fairness assessments

Responding to Erroneous Decisions

• Identifying systematic decision errors
• Implementing containment measures and pausing autonomy
• Addressing remediation and customer compensation
• Performing root cause analysis and updating controls

Assurance Review of Decision Layers

• Identifying evidence required by auditors
• Validating claims regarding accuracy and calibration
• Sampling decisions and manual overrides
• Reporting findings to the board of directors