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Duration 14 hours
Course Outline
Transformative Implications of Model-Driven Decision-Making
- Transition from heuristic rules and subjective judgment to algorithmic scoring
- Enhanced velocity and volume in automated execution
- Emerging failure vectors and deficits in institutional accountability
Decision Inventory and Risk Taxonomy
- Systematic cataloging of automated determinations
- Assessment of impact, reversibility, and stakeholder implications
- Risk stratification and associated control requirements
- Assignment of ownership for specific decision categories
Accountability and Human Oversight Frameworks
- Distinguishing substantive review from nominal human involvement
- Defining autonomy limits as core governance mechanisms
- Establishing escalation pathways and response deadlines
- Designating accountable officials and approval protocols
Decision Documentation and Explainability Standards
- Components of a defensible audit trail
- Retention of confidence metrics, alternative options, and contextual data
- Traceability of model versions and lineage
- Providing transparent explanations to the public or regulatory bodies
Policy Enforcement and Boundary Management
- Regulating permitted options and implementing change controls
- Governing thresholds and conducting periodic reviews
- Defining prohibited actions and implementing hard stops
- Integrating policy logic alongside model outputs
Regulatory Compliance Requirements
- Obligations under the EU AI Act for decision-making systems
- Industry-specific regulations in financial, insurance, and health sectors
- Data privacy mandates and rights regarding automated decisions
- Maintenance of documentation and conformity evidence
Vendor, Licensing, and Provenance Risks
- Comparison of hosted decision APIs versus open-weight models
- Licensing constraints for derivative and embedded models
- Risks associated with supplier reliance and service discontinuation
- Integrity of weights and version pinning strategies
Bias Mitigation, Fairness, and Outcome Surveillance
- Monitoring disparities in outcomes across demographic groups
- Identifying proxy variables and biases in option framing
- Analyzing appeal and complaint indicators
- Conducting periodic fairness assessments
Incident Response for Erroneous Decisions
- Detection of systematic decisional errors
- Containment measures and suspension of autonomous authority
- Remediation actions and citizen redress procedures
- Root cause analysis and control enhancements
Assurance Reviews of Decision Layers
- Evidence required for auditor verification
- Testing accuracy metrics and calibration claims
- Sampling of decisions and human overrides
- Reporting findings to executive leadership
Requirements
- Working familiarity with AI or automation initiatives in an enterprise environment.
- Understanding of risk management, compliance, or audit processes.
- No prior programming experience is required.
Target Audience
- Risk, compliance, and model risk officers
- Internal auditors overseeing automated decisions
- AI governance and data protection leads
- Product owners responsible for automated workflows
- Architects integrating oversight into agent systems
