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Advanced · Self-paced learning

AI Risk Management and Control Design

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An AI system sorts applications. An incorrect rejection can leave an eligible application unprocessed. Test whether a second professional review catches that error before dispatch, and prepare a control sheet for your own use case.

Work through case applications with known errors, count those found and missed, and check whether sufficient time is available for review. Adjust the control procedure or conditions of use and compare your result with the case explanation. For your own use case or the example, record risks, control steps, responsibilities and responses to deviations.

Take away your completed control sheet and error assessment. The exercise does not establish effectiveness in live operations. Binding risk acceptance requires a decision by authorised management and review of applicable requirements.

What you will work on

  • Assess AI risks for a specific use case using causes and consequences
  • Justify proposed acceptance boundaries and escalation triggers
  • Compare different controls against the same error sequence

Who it is for

  • Risk managers
  • Compliance managers
  • AI governance and control owners

Topics

  • Use boundaries and affected people
  • Risk scenarios and assessment uncertainty
  • Risk appetite, use boundaries and decision rights
  • Control effects and evidence requirements
  • Residual risk, monitoring and escalation

Your result

Your AI control sheet records what an error could cause, the limits of use, how to check the output and how to respond to deviations. It includes your assessment of the case applications and can be adapted to your use case.

Prerequisites

Basic risk management knowledge and a describable AI use case. Anonymised information is sufficient; a case study is planned.

Format and approach

Planned: 6–8 hours of self-study, including case decisions and self-checks with explanations.

Planned learning time: 6–8 hours

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