Isaca AAISM Exam - Topic 3 Question 10 Discussion

Actual exam question for Isaca's AAISM exam

Question #: 10
Topic #: 3

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

DAI ethical impact and user feedback metrics

BAI model complexity and accuracy metrics

AAI system availability and downtime metrics

CAI explainability reports and bias metrics

Show Suggested Answer

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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Kiera

1 day 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

6 days 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

11 days 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

17 days 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

22 days 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

27 days 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

2 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

2 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

2 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

2 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

2 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

3 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

3 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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