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| Section | Objectives |
|---|---|
| Topic 1: AI Operations | |
| Topic 2: AI Governance and Risk | |
| Topic 3: AI Auditing Tools and Techniques |
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NEW QUESTION # 180
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?
Answer: B
Explanation:
The AAIATM Study Guide reinforces that regular ethical reviews are essential to uphold human rights, prevent discriminatory outcomes, and ensure systems function within the boundaries of fairness and legality. While aligning with values (B) and preventing drift (D) are secondary benefits, the primary ethical imperative is the protection of individuals' rights and freedoms.
"Ethical reviews ensure AI systems do not violate rights related to privacy, fairness, access, and due process. This is foundational in building public trust and avoiding legal liabilities." Option C is the clearest expression of this responsibility. Performance and alignment with values are important but secondary to ensuring human-centric safeguards.
NEW QUESTION # 181
An IS auditor observes that an AI-based fraud detection system used by an insurance organization produces inconsistent outcomes when processing similar cases. Which of the following is the auditor ' s MOST efficient recommendation?
Answer: C
Explanation:
Inconsistent outcomes for similar cases often indicate issues with " Model Stability " or " Data Quality " . The most efficient recommendation is to " Analyze the training data and model evaluation parameters " to identify if the model was trained on biased, noisy, or non-representative datasets. Inconsistencies may also stem from poorly tuned hyperparameters that cause the model to react erratically to minor variations in input. While a human-in-the-loop (Option A) provides a safeguard, it is resource-intensive and does not address the root cause. Investigating the data and parameters allows the organization to fix the underlying logic of the model for long-term consistency.
NEW QUESTION # 182
Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?
Answer: C
Explanation:
To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIA™ Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries.
"Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved." While open-source usage (C) is a concern, only B provides legal clarity. Metrics (A) and logs (D) reflect performance-not legal compliance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Rights, Licensing, and Intellectual Property"
NEW QUESTION # 183
Which of the following is the MOST appropriate control for ensuring accountability when an AI system makes an erroneous high-impact decision?
Answer: C
Explanation:
Accountability requires a defined process -- someone must be responsible for reviewing, explaining, and remediating erroneous high-impact AI decisions; disclaimers alone do not constitute a control.
NEW QUESTION # 184
Which of the following BEST helps an organization manage bias in AI model decisions?
Answer: B
Explanation:
Bias is often subtle and context-dependent, making it difficult for automated systems to detect on their own. " Human oversight and feedback mechanisms " (Human-in-the-loop) allow domain experts to review model decisions and flag outcomes that appear discriminatory or unfair. According to the AAIA™ framework, these human feedback loops are critical for " correcting " the model ' s logic over time. While standardizing criteria (Option A) is a good starting point, humans provide the necessary " common sense " and ethical judgment that machines lack. Retraining (Option D) without human-validated data may simply reinforce the existing biases rather than eliminate them.
NEW QUESTION # 185
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