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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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Previous Lesson

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

AI Training for Internal Auditors: Auditing Models, Data and Decisions With Confidence
Auditing Data: Sourcing, Quality, Consent and Retention
Quiz: Auditing Data: Sourcing, Quality, Consent and Retention

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Question 1 of 5

1. Question

What should a data sourcing document include?

Only the names of data vendors
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Each source of data, when acquired, legal basis for use, and whether it was used for its original purpose
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Only data storage locations
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Only the cost of each data source
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Question 2 of 5

2. Question

What is required under the UK GDPR for automated decision-making involving personal data?

Only vendor approval
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A data protection impact assessment (DPIA)
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Only the data owner's signature
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A board approval
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Question 3 of 5

3. Question

How can you quickly surface data quality issues without being a data scientist?

By reading data quality reports written by the data team
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By asking the team about quality
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By randomly sampling records and checking for obvious errors
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By reviewing the code that cleans the data
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Incorrect / 1 Points
Question 4 of 5

4. Question

Does consent obtained for marketing automatically cover use of data in AI systems?

Yes, any consent covers all uses
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No, consent must be specific and informed about the new use
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Only if the data is anonymised
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Only if the model is not used for decisions
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Question 5 of 5

5. Question

What audit step should you take if data opt-out rights are claimed?

Accept the claim without verification
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Verify that the opt-out is actually working and data for opted-out individuals is not in the training set
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Only check opt-out policies, not actual data
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Ask the vendor whether they honour opt-out
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