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Duration 14 hours
Course Outline
Shifts in Dynamics When Judgments Become Model-Driven
- Transitioning from rule-based and human-judged methods to scored alternatives
- The velocity and volume of automated actions
- Emerging failure patterns and gaps in accountability
Judgment Cataloguing and Risk Categorisation
- Documenting automated judgments
- Assessing impact, reversibility and affected stakeholders
- Risk stratification and corresponding control requirements
- Assigning ownership to each judgment category
Responsibility and Human Supervision
- Distinguishing between substantive and perfunctory human review
- Treating autonomy thresholds as governance choices
- Defining escalation pathways and response deadlines
- Identifying the responsible owner and approval workflow
Decision Logs and Transparency
- Components of a robust and defensible decision log
- Retention of confidence scores, options and contextual data
- Traceability of models and versions
- Communicating judgment rationales to clients or regulatory bodies
Policy and Constraint Management
- Approval of option pools and change management protocols
- Supervision of thresholds and scheduled reviews
- Prohibited actions and absolute safeguards
- Implementing policy as code in conjunction with model outputs
Regulatory Requirements
- EU AI Act obligations for judgment systems
- Sector-specific regulations in finance, insurance and healthcare
- Data privacy standards and rights regarding automated judgments
- Documentation and evidence of compliance
Risks Related to Vendors, Licences and Provenance
- Hosted decision APIs versus open-weight models
- License conditions for derivative and embedded models
- Vendor reliance and risks of service discontinuation
- Integrity of weights and version pinning strategies
Bias, Equity and Outcome Monitoring
- Disparities in outcomes across different groups
- Proxy variables and the framing of options
- Signals from appeals and complaints
- Regular equity assessments
Responding to Erroneous Judgments
- Identifying systematic errors in judgment
- Containing issues and suspending autonomy
- Remediation measures and client compensation
- Root cause analysis and control refinement
Assurance Review of a Judgment Layer
- Evidence typically requested by auditors
- Validating claims regarding accuracy and calibration
- Sampling judgments and manual overrides
- Reporting findings to the board of directors
Requirements
- General familiarity with AI or automation initiatives within a corporate environment.
- Knowledge of risk management, compliance or auditing procedures.
- No prior programming knowledge is necessary.
Target Participants
- Risk, compliance and model risk managers
- Internal auditors examining automated judgments
- Leaders in AI governance and data privacy
- Product owners responsible for automated processes
- Architects integrating oversight mechanisms into agent systems
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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