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ISO 42001 Internal Auditor Course: Plan, Run and Report an AI Management System Audit

What Clause 9.2 Expects From an Internal Audit Programme

Quiz: What Clause 9.2 Expects From an Internal Audit Programme

Audit Principles From ISO 19011 Applied to AI Systems

Quiz: Audit Principles From ISO 19011 Applied to AI Systems

Building a Risk Based Annual Audit Programme

Quiz: Building a Risk Based Annual Audit Programme

Writing an Audit Plan, Scope and Criteria for an AI Management System

Quiz: Writing an Audit Plan, Scope and Criteria for an AI Management System

Preparing Checklists From Clauses 4 to 10 and Annex A

Quiz: Preparing Checklists From Clauses 4 to 10 and Annex A

Sampling Evidence: Model Cards, Logs, Datasets and Approvals

Quiz: Sampling Evidence: Model Cards, Logs, Datasets and Approvals

Interviewing Data Scientists Without Getting Lost in the Maths

Quiz: Interviewing Data Scientists Without Getting Lost in the Maths

Auditing Data Governance and Training Data Provenance

Quiz: Auditing Data Governance and Training Data Provenance

Auditing Human Oversight and Escalation in Live Operations

Quiz: Auditing Human Oversight and Escalation in Live Operations

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Quiz: Interviewing Data Scientists Without Getting Lost in the Maths

ISO 42001 Internal Auditor Course: Plan, Run and Report an AI Management System Audit
Interviewing Data Scientists Without Getting Lost in the Maths
Quiz: Interviewing Data Scientists Without Getting Lost in the Maths

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Question 1 of 5

1. Question

What is the primary purpose of interviewing data scientists during an ISO 42001 audit?

To evaluate the mathematical sophistication of the model
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To understand governance processes: approval workflows, data provenance, monitoring and change control
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To judge whether the data scientist is competent at machine learning
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To verify that the model has achieved perfect accuracy
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Question 2 of 5

2. Question

A data scientist says ‘We used L2 regularisation to avoid overfitting.’ As an auditor without deep statistical knowledge, what should you do?

Accept the answer and move to the next question
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Ask them to translate into simpler language so you can verify the concept is understood
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Assume the term is incorrect and report it as a finding
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Skip this topic entirely because you do not understand the mathematics
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Question 3 of 5

3. Question

During an interview, a data scientist frequently uses phrases like ‘I think,’ ‘probably,’ ‘I am not sure,’ and ‘no one has asked me that before.’ What does this signal?

The data scientist is being appropriately cautious
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Uncertainty suggesting gaps in governance, monitoring or competence awareness
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The data scientist is being cooperative and honest
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No concerns because precision is not important in interviews
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Question 4 of 5

4. Question

Which question reveals most about approval governance for a deployed model?

What is the mathematical architecture of your model?
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Walk me through how you got approval to train this model and who approved it before production deployment
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Does your model achieve 95 percent accuracy?
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How many lines of code did you write?
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Question 5 of 5

5. Question

Why should an auditor frame the interview by saying ‘We are auditing the AI management system, not evaluating your technical competence’?

Because technical competence is never relevant to audits
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Because framing the interview as collaborative rather than adversarial makes data scientists more willing to give honest answers about governance
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Because auditors are not qualified to discuss any technical matters
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Because this statement is required by ISO 42001
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