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NEW QUESTION # 70
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?
Answer: A
Explanation:
Using postal codes as a primary factor in credit scoring raises concerns of geographic and socioeconomic bias. Postal codes can serve as proxies for race, ethnicity, or income level-potentially violating fair lending laws and ethical guidelines.
"Auditors should flag models that rely on proxies for protected attributes. Postal codes are high-risk features that may inadvertently lead to redlining or other discriminatory practices." A, B, and C are more justifiable under fair lending guidelines. Thus, D presents the highest concern.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Bias and Fairness in Financial AI Models"
NEW QUESTION # 71
An organization is evaluating whether to classify a new AI use case as "high-risk" under its internal AI risk framework. Which factor is MOST relevant to this determination?
Answer: C
Explanation:
Risk classification frameworks (including the EU AI Act's approach) generally tier risk based on the potential impact of the AI system's decisions on individuals, not on technical implementation details.
NEW QUESTION # 72
A retail organization uses an AI model to analyze customers' purchase history in order to offer personalized discounts. Which of the following practices represents the MOST ethical use of customer data?
Answer: C
Explanation:
The ethical use of customer data is rooted in respecting privacy, maintaining informed consent, and enabling data subjects to exercise control over their personal information. The AAIATM Study Guide clearly outlines that obtaining explicit consent and providing opt-out capabilities align with principles of data protection and ethical AI.
"Ethical AI implementation includes transparency in data collection, clear consent mechanisms, and the right of users to opt out or control their personal data usage. Retail and consumer applications must ensure that personalized services do not override these data subject rights."
NEW QUESTION # 73
An IS auditor is reviewing change documentation of an AI model. Which of the following would pose the GREATEST risk?
Answer: B
Explanation:
A low number of test scenarios (C) indicates insufficient testing coverage, creating a high likelihood that errors, biases, or edge-case failures remain undetected. AI systems require broad, diverse scenario testing, including fairness, robustness, and stress testing. AAIA highlights comprehensive testing as essential to AI reliability and risk mitigation.
NEW QUESTION # 74
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?
Answer: A
Explanation:
In predictive maintenance use cases--such as detecting turbine failure--the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIATM Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.
"Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives." Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.
NEW QUESTION # 75
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