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Isaca AAISM Exam - Topic 3 Question 10 Discussion

Which option best BEST represents a combination of quantitative and qualitative metrics that can be used to comprehensively evaluate AI transparency?
D) AI ethical impact and user feedback metrics
B) AI model complexity and accuracy metrics
A) AI system availability and downtime metrics
C) AI explainability reports and bias metrics

Isaca AAISM Exam - Topic 3 Question 10 Discussion

Actual exam question for Isaca's AAISM exam
Question #: 10
Topic #: 3
[All AAISM Questions]

Which option best BEST represents a combination of quantitative and qualitative metrics that can be used to comprehensively evaluate AI transparency?

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Suggested Answer: D

The AAISM governance framework emphasizes that AI transparency cannot be evaluated using only technical statistics; it requires a combination of quantitative and qualitative metrics. The best pairing is ethical impact assessments (qualitative) with user feedback metrics (quantitative and perception-based). Availability and accuracy metrics measure performance, not transparency. Explainability reports and bias metrics are useful but still technical and limited. Comprehensive evaluation of transparency requires consideration of ethical dimensions and stakeholder perspectives, which is achieved through ethical impact analysis and user feedback.


AAISM Study Guide -- AI Governance and Program Management (Transparency and Accountability)

ISACA AI Security Management -- Measuring Ethical AI Practices

by Hermila at Jan 25, 2026, 10:02 AM

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Walker
6 days ago
I agree with Rozella. Combining both types gives a fuller picture.
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Whitley
11 days ago
Option B has solid metrics but lacks qualitative insights.
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Dahlia
16 days ago
I prefer option A. It focuses more on user experience, which is crucial.
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Rozella
2 months ago
I think option C is the best. It balances numbers and user feedback.
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Kimbery
2 months ago
Quantitative metrics alone won't cut it, need the full picture.
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Gayla
2 months ago
Don't forget about ethical considerations too!
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Millie
3 months ago
Wait, can qualitative metrics really be measured?
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Cora
3 months ago
Totally agree, it's all about balance.
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Quentin
3 months ago
You need both data accuracy and user feedback!
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Kiera
3 months ago
Option D is just too vague. You need specifics to properly assess AI transparency, not just a general "combination of metrics."
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Tarra
3 months ago
I'd go with option C. Gotta have that perfect blend of numbers and human insight to get the full picture.
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Laurene
3 months ago
Haha, option A? Really? Quantitative metrics alone? That's like trying to measure the Mona Lisa's smile with a ruler.
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Lenna
4 months ago
Option B seems like a good choice, but I think it's missing the qualitative aspect that's essential for a comprehensive evaluation.
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Stefany
4 months ago
The correct answer is clearly option C. Combining both quantitative and qualitative metrics is the only way to truly evaluate AI transparency.
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Vince
4 months ago
Combining metrics sounds tricky! I wonder if we should prioritize one type over the other, or if there's a framework we can use to balance them?
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Melinda
5 months ago
I feel like we covered this in class, but I can't recall the specific metrics. I think we mentioned something about interpretability scores too?
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Leontine
5 months ago
I remember a practice question that asked about metrics for AI ethics. Maybe something similar applies here, like using fairness scores alongside user trust surveys?
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Tricia
5 months ago
I think we discussed how qualitative metrics like user feedback could complement quantitative data like accuracy rates, but I'm not sure how to combine them effectively.
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Dalene
5 months ago
I'm a bit unsure how to approach this. I guess I'd start by listing out potential quantitative and qualitative metrics, then try to find a combination that seems like it would provide a thorough evaluation. But it's not an easy question.
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Ming
5 months ago
Okay, let me think this through step-by-step. First, I'd want to identify the key dimensions of AI transparency that need to be measured. Then I'd look for a set of metrics, both numerical and descriptive, that could capture those dimensions holistically.
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Karima
6 months ago
Hmm, this is a tricky one. I'd need to really think through what kinds of factors are important for evaluating AI transparency. Quantitative and qualitative data would both be crucial, but finding the right mix could be challenging.
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Belen
6 months ago
I think I'd start by brainstorming different types of metrics that could be relevant, both quantitative and qualitative. Then I'd try to identify a combination that could provide a comprehensive evaluation.
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Ayesha
13 hours ago
I agree, mixing metrics is key for a full picture.
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