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

What Changes When Decisions Become Model-Driven

• Transitioning from rigid rules and human discretion to scored, probabilistic options
• The rapid pace and volume of automated actions
• Emerging failure modes and potential gaps in accountability

Decision Inventory and Risk Classification

• Building a comprehensive catalogue of automated decisions
• Evaluating impact, reversibility, and the parties affected
• Establishing risk tiers and corresponding control expectations
• Assigning clear ownership for each decision class

Accountability and Human Oversight

• Distinguishing between meaningful and nominal human review
• Treating autonomy thresholds as deliberate governance choices
• Defining escalation pathways and response time requirements
• Identifying the accountable owner and sign-off process

Decision Records and Explainability

• Components of a defensible decision record
• Retaining confidence scores, available options, and contextual data
• Ensuring traceability of models and their versions
• Articulating decisions to customers or regulatory bodies

Policy and Boundary Control

• Managing approved option sets through strict change control
• Governing thresholds and conducting periodic reviews
• Identifying prohibited decisions and implementing hard stops
• Implementing policy as code in parallel with model outputs

Regulatory Expectations

• Obligations under the EU AI Act for decision systems
• Specific sector rules in finance, insurance, and healthcare
• Data protection requirements and rights regarding automated decisions
• Requirements for documentation and evidence of conformity

Vendor, Licence and Provenance Risk

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

Bias, Fairness and Outcome Monitoring

• Detecting outcome disparities across different groups
• Identifying proxy variables and biases in option framing
• Utilising appeal and complaint data as signals
• Conducting periodic fairness assessments

Incident Response for Wrong Decisions

• Identifying systematic errors in decision-making
• Containing incidents by suspending automated autonomy
• Executing remediation and providing customer redress
• Performing root cause analysis and updating controls

Assurance Review of a Decision Layer

• Defining the evidence an auditor should request
• Testing claims regarding accuracy and calibration
• Sampling specific decisions and manual overrides
• Presenting findings to the board