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NEW QUESTION # 294
An IS auditor reviewing the latest AI chatbot release identifies that, despite high accuracy rates, non-English users complain about the model ' s poor accuracy. Which of the following controls is BEST at ensuring detection of subgroup regressions?
Answer: D
Explanation:
A common pitfall in AI performance is " aggregate accuracy, " which masks poor performance in specific demographics or subgroups. To detect these " subgroup regressions, " auditors should look for " comparative evaluations " where the model is tested separately for different languages. " Parity thresholds " establish the maximum allowable difference in performance between the majority group (English) and minority groups.
This ensures fairness and consistent user experience. Post-go-live human review is reactive, and translating everything to English ignores the nuances of the original language that likely caused the errors in the first place.
NEW QUESTION # 295
Which of the following would be MOST useful for an IS auditor when testing high-impact rare scenarios that have not yet occurred in a production environment?
Answer: A
Explanation:
For "edge cases" or "rare scenarios" (often called "black swan" events), historical data is insufficient because the events haven't happened yet. "Synthetic data" is artificially generated data that maintains the statistical properties of real data but includes simulated rare events. This allows auditors to "stress test" how the AI model would react to extreme conditions, such as a massive market crash or a rare medical emergency. Anonymized or de-identified data only reflects past occurrences. Using synthetic data is a proactive testing strategy to ensure the AI's safety and robustness in unpredictable environments.
NEW QUESTION # 296
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?
Answer: C
Explanation:
Unchecked AI outputs pose a significant risk because incorrect or biased results may propagate into decision-making processes. The AAIATM Study Guide stresses that validating AI outputs for consistency, accuracy, and reasonableness is a critical responsibility of both operators and auditors.
"Auditors must verify that appropriate output validation mechanisms are in place. Failure to check outputs increases the likelihood of undetected errors influencing downstream processes or decisions."
NEW QUESTION # 297
When auditing the transparency of an AI system, which of the following would be the MOST effective way to understand the model's decision-making process?
Answer: A
Explanation:
Transparency in AI systems is a key requirement to ensure trust, accountability, and ethical compliance.
According to the ISACA AAIA™ Study Guide under the "AI Governance and Risk Management" section, understanding the decision-making process of an AI system falls under the principle of explainability.
Explainability refers to the degree to which an observer can understand the internal mechanics of an AI system and the rationale behind its outputs.
"Reviewing the explainability of AI outputs allows auditors and stakeholders to determine whether model decisions are interpretable and justifiable. High transparency means stakeholders can trace how and why a decision was made." While algorithm complexity and computational cost are technical considerations, they do not directly facilitate the audit of decision-making transparency. Similarly, training data diversity is essential for bias reduction but does not explain how decisions are derived. Therefore, option D is the most aligned with auditing transparency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Transparency and Explainability"
NEW QUESTION # 298
Which of the following should be an IS auditor's GREATEST concern if class imbalance is identified in training data for an AI model?
Answer: B
Explanation:
Class imbalanceoccurs when one or more classes are underrepresented in the training data. The GREATEST concern ismodel bias(C): the model may learn to favor the majority class, leading to poor performance and unfair treatment for minority classes. In high-stakes applications (e.g., fraud detection, credit scoring, medical diagnosis), this can translate intosystematic discrimination or incorrect decisions. AAIA highlights class imbalance as a common source of bias and stresses mitigation techniques (resampling, reweighting, threshold adjustments).
Data drift (A) refers to changes in data distributions over time-related but distinct. Data quality (B) is broader and may or may not be affected by imbalance. Overfitting (D) is a risk, but class imbalance more directly raises fairness and representativeness concerns rather than overfitting alone. Thus,bias arising from class imbalanceis the auditor's primary concern.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (data balancing, bias risk).
ISACA AI ethics and model risk guidance discussing class imbalance and fairness.
NEW QUESTION # 299
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