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

Shifting the Landscape: When Decisions Become Model-Driven

• Transitioning from rigid rules and human discretion to probabilistic scoring
• The increased velocity and volume of automated actions
• Emerging failure patterns and potential accountability voids

Mapping the Decision Landscape and Risk Profiling

• Systematically cataloguing all automated decisions
• Evaluating impact, reversibility, and stakeholder implications
• Establishing risk tiers and corresponding control standards
• Assigning clear ownership for each decision category

Establishing Accountability and Human Supervision

• Distinguishing between substantive and superficial human review
• Setting autonomy limits as deliberate governance choices
• Defining escalation pathways and service-level response times
• Identifying the responsible owner and sign-off authority

Maintaining Decision Logs and Transparency

• Components of a robust, defensible decision record
• Preserving confidence scores, alternative options, and contextual data
• Ensuring traceability of model versions and lineage
• Communicating decision rationale to clients or regulatory bodies

Enforcing Policy and Boundary Management

• Governing approved option sets and managing changes
• Overseeing threshold adjustments through regular reviews
• Identifying prohibited actions and implementing hard stops
• Integrating policy-as-code with model outputs

Meeting Regulatory Requirements

• Compliance with EU AI Act standards for decision systems
• Adhering to industry-specific regulations in finance, insurance, and healthcare
• Upholding data privacy rights regarding automated decisions
• Maintaining comprehensive documentation and conformity evidence

Managing Vendor, License, and Provenance Risks

• Comparing hosted decision APIs with open-weight models
• Reviewing license terms for derivative and embedded models
• Assessing supplier reliance and risks of service discontinuation
• Ensuring weight integrity through version pinning

Monitoring Bias, Fairness, and Outcomes

• Tracking outcome disparities across demographic groups
• Identifying proxy variables and issues in option framing
• Analyzing signals from appeals and customer complaints
• Conducting regular fairness audits

Responding to Incinents: Handling Erroneous Decisions

• Detecting patterns of systematic decision errors
• Containing risks by suspending autonomous functions
• Implementing remediation strategies and customer compensation
• Analyzing root causes and updating controls

Conducting Assurance Reviews of Decision Layers

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