Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
The Shift to Model-Driven Decisions
- Transitioning from rule-based human judgement to scored options
- The pace and scale of automated actions
- Emerging failure modes and accountability challenges
Decision Inventory and Risk Assessment
- Mapping automated decisions systematically
- Considering impact, reversibility and stakeholder effects
- Defining risk tiers and corresponding control requirements
- Assigning ownership for each decision class
Accountability and Human Supervision
- Distinguishing between meaningful and nominal human review
- Viewing autonomy thresholds as governance choices
- Establishing escalation pathways and response times
- Identifying accountable owners and approval processes
Decision Records and Transparency
- Components of a defensible decision record
- Retaining confidence scores, options and contextual data
- Tracing model versions and lineage
- Communicating decisions to customers or regulators
Policy and Boundary Management
- Managing approved option sets and change controls
- Governing thresholds and conducting periodic reviews
- Defining prohibited decisions and hard stops
- Implementing policy as code alongside model outputs
Regulatory Requirements
- Obligations under the EU AI Act for decision systems
- Industry-specific rules in finance, insurance and healthcare
- Data protection standards and rights regarding automated decisions
- Maintaining documentation and evidence of conformity
Vendor, Licence and Provenance Risks
- Comparing hosted decision APIs with open-weight models
- Understanding licence terms for derivative and embedded models
- Assessing supplier reliance and discontinuation risks
- Ensuring weight integrity and version locking
Bias, Fairness and Performance Monitoring
- Identifying outcome disparities across different groups
- Analyzing proxy variables and option presentation
- Monitoring appeal and complaint indicators
- Conducting regular fairness evaluations
Incident Management for Erroneous Decisions
- Detecting systemic decision errors
- Containing incidents and suspending autonomous capabilities
- Implementing remediation and customer compensation
- Analyzing root causes and updating controls
Assurance Review of the Decision Layer
- Specifying evidence for auditor review
- Validating accuracy and calibration assertions
- Sampling decisions and human overrides
- Reporting findings to senior leadership
Requirements
- Basic familiarity with AI or automation initiatives within an enterprise context.
- General understanding of risk, compliance or audit procedures.
- No coding experience is necessary.
Target Audience
- Risk, compliance and model risk specialists
- Internal auditors assessing automated decisions
- AI governance and data protection leaders
- Product owners responsible for automated workflows
- Architects integrating oversight into agent systems
