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

Shifting to Model-Driven Decision Making

• Transitioning from static rules and human judgment to scored options.
• Managing the speed and scale of automated actions.
• Identifying new failure modes and accountability gaps.

Inventorying and Classifying Decision Risks

• Creating a comprehensive catalogue of automated decisions.
• Assessing impact, reversibility, and stakeholder effects.
• Establishing risk tiers and corresponding control expectations.
• Assigning ownership for each decision class.

Accountability and Human Oversight

• Distinguishing between meaningful and nominal human review.
• Setting autonomy thresholds as core governance decisions.
• Defining escalation pathways and response timelines.
• Identifying accountable owners and approval sign-offs.

Decision Records and Explainability

• Defining the components of a defensible decision record.
• Retaining confidence scores, options, and contextual data.
• Ensuring traceability of models and versions.
• Communicating decisions effectively to customers and regulators.

Policy and Boundary Management

• Governing approved option sets and managing change control.
• Implementing threshold governance and periodic reviews.
• Defining prohibited decisions and enforcing hard stops.
• Integrating policy-as-code alongside model outputs.

Regulatory Compliance Requirements

• Adhering to EU AI Act obligations for decision systems.
• Complying with sector-specific rules in finance, insurance, and healthcare.
• Addressing data protection and rights regarding automated decisions.
• Maintaining documentation and evidence of conformity.

Vendor, Licensing, and Provenance Risks

• Evaluating hosted decision APIs versus open-weight models.
• Reviewing license terms for derivative and embedded models.
• Mitigating supplier dependency and discontinuation risks.
• Ensuring weight integrity and version pinning.

Bias, Fairness, and Outcome Monitoring

• Detecting outcome disparities across different groups.
• Identifying proxy variables and biases in option framing.
• Utilizing appeal and complaint signals for feedback.
• Conducting periodic fairness assessments.

Incident Response for Erroneous Decisions

• Detecting systematic errors in decision-making.
• Containing incidents and suspending autonomous actions.
• Implementing remediation and providing customer redress.
• Performing root cause analysis and updating controls.

Assurance Review of Decision Layers

• Identifying evidence required by auditors.
• Verifying accuracy and calibration claims through testing.
• Sampling decisions and override actions.
• Reporting findings to the board of directors.