Intrati in legatura
 Durata 14 ore

Schița de curs

What Changes When Decisions Become Model-Driven

  • From rules and human judgement to scored options
  • Speed and scale of automated action
  • New failure modes and accountability gaps

Decision Inventory and Risk Classification

  • Cataloguing automated decisions
  • Impact, reversibility and affected parties
  • Risk tiers and control expectations
  • Ownership per decision class

Accountability and Human Oversight

  • Meaningful versus nominal human review
  • Autonomy thresholds as governance decisions
  • Escalation routes and response times
  • Accountable owner and sign-off

Decision Records and Explainability

  • What a defensible decision record contains
  • Confidence, options and context retention
  • Model and version traceability
  • Explaining a decision to a customer or regulator

Policy and Boundary Control

  • Approved option sets and change control
  • Threshold governance and periodic review
  • Prohibited decisions and hard stops
  • Policy as code alongside model output

Regulatory Expectations

  • EU AI Act obligations for decision systems
  • Sector rules in finance, insurance and health
  • Data protection and automated decision rights
  • Documentation and conformity evidence

Vendor, Licence and Provenance Risk

  • Hosted decision APIs versus open weights
  • Licence terms for derivative and embedded models
  • Supplier dependency and discontinuation risk
  • Weight integrity and version pinning

Bias, Fairness and Outcome Monitoring

  • Outcome disparity across groups
  • Proxy variables and option framing
  • Appeal and complaint signals
  • Periodic fairness review

Incident Response for Wrong Decisions

  • Detecting systematic decision error
  • Containment and suspension of autonomy
  • Remediation and customer redress
  • Root cause and control update

Assurance Review of a Decision Layer

  • Evidence an auditor should request
  • Testing accuracy and calibration claims
  • Sampling decisions and overrides
  • Reporting findings to the board

Cerințe

  • Familiaritate cu proiectele de AI sau automatizare într-un context enterprise.
  • Înțelegerea proceselor de risc, conformitate sau audit.
  • Înțelegerea proceselor de risc, conformitate sau audit. Nu este necesară experiența în programare.

Público Țintă

  • Ofițeri de risc, conformitate și risc de model
  • Auditori interni care review-uiesc deciziile automate
  • Leaders în guvernanță AI și protecția datelor
  • Product owners responsabilizați pentru fluxurile de lucru automate
  • Arhitecți care proiectează supravegherea în sistemele de agenți

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