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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: Understanding the Model Life Cycle Well Enough to Audit It

AI Training for Internal Auditors: Auditing Models, Data and Decisions With Confidence
Understanding the Model Life Cycle Well Enough to Audit It
Quiz: Understanding the Model Life Cycle Well Enough to Audit It

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Question 1 of 5

1. Question

In the model life cycle, which stage involves selecting data sources and handling consent for personal data?

Problem definition and scoping
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Data preparation and quality
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Model deployment and monitoring
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Validation and testing
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Question 2 of 5

2. Question

What is model drift and why does it matter for audit?

Changes to the model code made by developers
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The model's performance degrading over time because the real world has changed in ways training data did not predict
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Differences in how different teams describe the same model
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Errors introduced during the data cleaning process
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Question 3 of 5

3. Question

During the development stage, what is a common fairness failure that overall accuracy metrics miss?

The model is too slow in production
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The data source is too expensive to maintain
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The model performs badly on specific subsets like people with disabilities, even though overall accuracy is high
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The model requires too much memory
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Question 4 of 5

4. Question

Who should conduct the independent validation of a model before deployment?

The same data scientist who built the model
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The model vendor
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Someone not involved in model building
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Only external consultants
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Question 5 of 5

5. Question

What should happen at the retirement stage for audit purposes?

All data should be deleted immediately
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Decisions made by the old system should still be reviewable, retention policies followed, and lessons documented
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The old system should be archived without review
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No documentation needs to be kept
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