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

Shifting Paradigms in Model-Driven Decision Making

• Transitioning from rule-based logic and human judgement to scored options
• The increased velocity and scale of automated actions
• Emerging failure modes and accountability voids

Decision Inventory and Risk Tiering

• Creating a comprehensive 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 between substantive and nominal human review
• Treating autonomy thresholds as governance choices
• Establishing escalation pathways and response timelines
• Identifying accountable owners and sign-off requirements

Decision Records and Explainability

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

Policy and Boundary Management

• Managing approved option sets and change controls
• Governing thresholds through periodic reviews
• Defining prohibited decisions and hard stops
• Implementing policy as code alongside model outputs

Regulatory Requirements

• Compliance with EU AI Act obligations for decision systems
• Industry-specific regulations in finance, insurance, and healthcare
• Data protection standards and rights regarding automated decisions
• Documentation and evidence of conformity

Vendor, Licence, and Provenance Risks

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

Bias, Fairness, and Outcome Monitoring

• Analyzing outcome disparities across different groups
• Identifying proxy variables and option framing issues
• Leveraging appeal and complaint signals
• Conducting periodic fairness assessments

Responding to Incorrect Decisions

• Detecting systematic decision errors
• Containing incidents and suspending autonomy
• Implementing remediation and customer redress
• Analyzing root causes and updating controls

Assurance Review of Decision Layers

• Evidence expected by auditors
• Validating accuracy and calibration claims
• Sampling decisions and overrides for review
• Reporting findings to the board