Styrning av automatiserade beslut: Revision, uppföljning och kontroll för AI-beslutsmodeller Träningskurs

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