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

Shifts in Decision-Making: The Move to Model-Driven Approaches

• Transition from rigid rules and human judgement to scored options
• The velocity and scale of automated actions
• Emerging failure modes and accountability gaps

Decision Inventory and Risk Categorisation

• Building a catalogue of automated decisions
• Evaluating impact, reversibility, and affected stakeholders
• Defining risk tiers and corresponding control expectations
• Assigning ownership for each decision class

Accountability and Human Supervision

• Distinguishing meaningful review from nominal oversight
• Setting autonomy thresholds as governance choices
• Establishing escalation pathways and response timelines
• Identifying accountable owners and approval workflows

Decision Records and Explainability

• Components of a defensible decision record
• Retaining confidence scores, available options, and context
• Tracing model versions and lineage
• Communicating decision logic to customers or regulators

Policy and Boundary Management

• Governing approved option sets and change controls
• Managing threshold governance and periodic reviews
• Implementing prohibited decisions and hard stops
• Integrating policy-as-code with model outputs

Regulatory Requirements

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

Vendor, Licensing, and Provenance Risks

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

Bias, Fairness, and Outcome Monitoring

• Analysing outcome disparities across different groups
• Identifying proxy variables and option framing issues
• Utilising appeal and complaint signals
• Conducting regular fairness assessments

Incident Response for Erroneous Decisions

• Detecting systematic decision errors
• Containing incidents and suspending autonomy
• Managing remediation and customer redress
• Performing root cause analysis and updating controls

Assurance Review of Decision Layers

• Defining evidence requirements for auditors
• Testing accuracy and calibration claims
• Sampling decisions and manual overrides
• Reporting findings to the board