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Duration 14 hours
Course Outline
Implications of Model-Driven Decision Making
- Transitioning from rule-based and human judgment to scored options
- The velocity and volume of automated actions
- Emerging failure patterns and accountability voids
Inventory of Decisions and Risk Categorization
- Creating a comprehensive catalog of automated decisions
- Assessing impact, reversibility and affected stakeholders
- Defining risk levels and expected control measures
- Assigning ownership for specific decision classes
Responsibility and Human Supervision
- Distinguishing between meaningful and nominal human review
- Treating autonomy thresholds as governance choices
- Defining escalation paths and response timelines
- Identifying accountable owners and approval requirements
Decision Documentation and Transparency
- Components of a robust and defensible decision record
- Retention of confidence scores, options and contextual data
- Traceability of models and specific versions
- Explaining decision logic to clients or regulatory bodies
Policy Enforcement and Boundary Management
- Managing approved option sets and change control processes
- Governing thresholds and conducting periodic reviews
- Establishing prohibited actions and hard stops
- Implementing policy as code alongside model outputs
Regulatory Requirements
- Obligations under the EU AI Act for decision systems
- Specific regulations in finance, insurance and healthcare sectors
- Data privacy laws and rights regarding automated decisions
- Documentation standards and evidence of conformity
Risks Associated with Vendors, Licenses and Provenance
- Comparing hosted decision APIs with open-weight models
- License terms for derivative and embedded models
- Supplier dependency and risks of discontinuation
- Integrity of model weights and version pinning
Bias, Fairness and Outcome Tracking
- Identifying outcome disparities across different groups
- Detecting proxy variables and biased option framing
- Monitoring appeal and complaint indicators
- Conducting regular fairness assessments
Response to Incorrect Decisions
- Identifying systematic decision errors
- Containing issues and suspending autonomous actions
- Remediation steps and client compensation
- Root cause analysis and control enhancements
Assurance Review of Decision Layers
- Evidence required by auditors
- Validating accuracy and calibration assertions
- Sampling decisions and manual overrides
- Reporting findings to the board of directors
Requirements
- General knowledge of AI or automation initiatives within enterprise environments.
- Familiarity with risk management, compliance or audit procedures.
- No prior programming expertise is necessary.
Intended Audience
- Risk managers, compliance officers and model risk specialists
- Internal auditors examining automated determinations
- Leads in AI governance and data protection
- Product owners responsible for automated workflows
- Architects integrating oversight mechanisms into agent systems
