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Isaca AAIA Exam - Topic 1 Question 22 Discussion

Which of the following pre-processing steps would MOST effectively justify an AI model's decision to a non-technical stakeholder?
B) Local interpretable model-agnostic explanations (LIME)
A) Permutation feature importance
C) Partial dependence plots
D) AI model penetration testing

Isaca AAIA Exam - Topic 1 Question 22 Discussion

Actual exam question for Isaca's AAIA exam
Question #: 22
Topic #: 1
[All AAIA Questions]

Which of the following pre-processing steps would MOST effectively justify an AI model's decision to a non-technical stakeholder?

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Suggested Answer: B

While all the listed techniques (except penetration testing) support interpretability, 'LIME' is specifically noted in the ISACA AAIA Study Guide for its ability to explain individual decisions. LIME creates a simpler, interpretable model around a specific prediction to show which features (e.g., high income or low debt) were the primary drivers for that specific case. This 'Local' explanation is much easier for non-technical stakeholders or customers to understand than 'Global' metrics like feature importance (Option A) or partial dependence plots (Option C), which describe the model's behavior as a whole.


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Mohammad
1 day ago
I’m not convinced LIME is always effective for non-techies.
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Rashad
6 days ago
A) Permutation feature importance is also useful, but not as straightforward.
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Devora
11 days ago
Wait, isn't D) AI model penetration testing more about security than explanations?
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Iola
17 days ago
Totally agree, LIME makes it super clear!
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Glory
22 days ago
I think B) LIME is the best choice for explaining AI decisions.
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Lon
27 days ago
I don't recall much about AI model penetration testing in this context; it seems more about security than explaining decisions.
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Brett
1 month ago
I feel like partial dependence plots could also be useful, but they might be too technical for some stakeholders.
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Brock
1 month ago
I'm not entirely sure, but I remember permutation feature importance being mentioned in class as a way to show how features impact predictions.
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Rosalyn
1 month ago
I think LIME might be the best choice since it provides local explanations that are easier for non-technical people to understand.
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