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AI Training for Internal Auditors: Auditing Models, Data and Decisions With Confidence

What Internal Audit Can and Cannot Assure About AI

Quiz: What Internal Audit Can and Cannot Assure About AI

Building AI Into the Annual Audit Plan

Quiz: Building AI Into the Annual Audit Plan

Understanding the Model Life Cycle Well Enough to Audit It

Quiz: Understanding the Model Life Cycle Well Enough to Audit It

Scoping an AI Audit: System, Process or Whole Programme

Quiz: Scoping an AI Audit: System, Process or Whole Programme

Auditing Governance: Policy, Roles and Approval Records

Quiz: Auditing Governance: Policy, Roles and Approval Records

Auditing Data: Sourcing, Quality, Consent and Retention

Quiz: Auditing Data: Sourcing, Quality, Consent and Retention

Auditing Model Development, Testing and Validation Records

Quiz: Auditing Model Development, Testing and Validation Records

Auditing Deployment Controls, Access and Change Management

Quiz: Auditing Deployment Controls, Access and Change Management

Auditing Monitoring, Drift Detection and Alert Response

Quiz: Auditing Monitoring, Drift Detection and Alert Response

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Quiz: What Internal Audit Can and Cannot Assure About AI

AI Training for Internal Auditors: Auditing Models, Data and Decisions With Confidence
What Internal Audit Can and Cannot Assure About AI
Quiz: What Internal Audit Can and Cannot Assure About AI

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Question 1 of 5

1. Question

What is the primary value internal audit provides in AI governance?

Evaluating the statistical performance of machine learning models
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Verifying whether governance and control frameworks are in place and working
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Making business trade-offs between competing demands on AI systems
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Redesigning model architectures to improve accuracy
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Question 2 of 5

2. Question

Which is an example of a first line control in AI governance?

A compliance team designing AI governance policy
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An audit team sampling hiring decisions to verify they were logged
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A model owner setting fairness thresholds for recruitment AI
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The risk committee approving high-risk AI use cases
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Question 3 of 5

3. Question

What governance mistake do many AI audits miss by starting with technical testing?

They ignore data quality issues
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They skip governance controls and go straight to model performance testing
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They focus too much on third party vendors
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They do not sample enough decision records
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Question 4 of 5

4. Question

Which system should typically be prioritised in a first AI audit?

An internal sales forecasting tool used by three people
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A hiring system that makes decisions affecting people at scale
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A deprecated AI system no longer in use
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A vendor-supplied tool with minimal business impact
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Question 5 of 5

5. Question

Can internal audit evaluate the statistical accuracy of a machine learning model in the way a data scientist can?

Yes, audit always requires detailed statistical analysis
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Yes, but only if the model operates in financial services
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No, that evaluation belongs to data scientists and model owners
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Only if the model handles personal data
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