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

Transforming Decision-Making: The Model-Driven Era

• Transitioning from rule-based logic and human judgement to scored options.
• The pace and scale of automated actions.
• Emerging failure modes and accountability gaps.

Decision Inventory and Risk Categorisation

• Compiling a catalogue of automated decisions.
• Evaluating impact, reversibility, and affected stakeholders.
• Establishing risk tiers and corresponding control expectations.
• Assigning ownership for each decision category.

Accountability and Human Oversight

• Distinguishing between meaningful and nominal human review.
• Setting autonomy thresholds as a governance strategy.
• Defining escalation pathways and response timelines.
• Identifying accountable owners and sign-off processes.

Decision Records and Explainability

• Components of a defensible decision record.
• Persisting confidence scores, options, and context.
• Ensuring model and version traceability.
• Articulating decisions to customers or regulators.

Policy and Boundary Management

• Managing approved option sets and change controls.
• Governing thresholds and conducting periodic reviews.
• Implementing prohibited decisions and hard stops.
• Integrating policy-as-code alongside model outputs.

Regulatory Requirements

• EU AI Act obligations relevant to decision systems.
• Sector-specific regulations in finance, insurance, and health.
• Data protection standards and automated decision rights.
• Documentation and evidence of conformity.

Vendor, Licensing, and Provenance Risks

• Comparing hosted decision APIs with open weights.
• Navigating licence terms for derivative and embedded models.
• Mitigating supplier dependency and discontinuation risks.
• Ensuring weight integrity and version pinning.

Bias, Fairness, and Outcome Monitoring

• Tracking outcome disparities across demographic groups.
• Analysing proxy variables and option framing.
• Interpreting signals from appeals and complaints.
• Conducting regular fairness assessments.

Incident Response for Erroneous Decisions

• Identifying systematic decision errors.
• Implementing containment and suspending autonomy where necessary.
• Managing remediation and customer redress.
• Conducting root cause analysis and updating controls.

Assurance Review of Decision Layers

• Compiling evidence for auditor review.
• Testing accuracy and calibration claims.
• Sampling decisions and manual overrides.
• Reporting findings to the board.