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NEW QUESTION # 76
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?
Answer: A
Explanation:
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide, "algorithmic transparency-enabling auditors and stakeholders to see and understand the rationale behind automated decisions-is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.
By contrast, options A, B, and D are either standard operational aspects or supportive features, but they do not directly empower auditors to evaluate or scrutinize the algorithm's decision-making process. The Study Guide explicitly highlights, "Effective AI governance frameworks require that systems provide clear, interpretable explanations for their decisions, supporting auditability and accountability across the AI lifecycle." Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Transparency and Explainability in AI Systems," Subsection: "Auditability and Accountability"
NEW QUESTION # 77
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: A
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 AAIATM 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."
NEW QUESTION # 78
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations.
Which of the following MOST effectively detects data drift related to the model's predictions?
Answer: A
Explanation:
Detecting data drift is critical in maintaining the reliability and accuracy of AI models, especially in dynamic environments like healthcare where patient populations and data characteristics can change over time.
According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide,data drift refers to changes in the input data's statistical properties compared to the data on which the model was originally trained.
If not detected, data drift can degrade model performance and lead to erroneous predictions.
Themost effective approach to detect data driftis tocontinuously compare the statistical distributions of incoming (production) data with those of the training dataset. This allows organizations to identify deviations in data patterns, which can be early indicators that the AI model's predictions may no longer be valid or optimal.
As stated in the AAIA™ Study Guide under "AI Model Monitoring and Maintenance":
"Monitoring input data for distributional changes compared to the model's training data is an essential step in identifying data drift. Statistical tests and visualizations can help auditors and AI operators detect when the underlying data characteristics have shifted, prompting further investigation or retraining needs." While options such as retraining the model (option C) or adversarial testing (option D) are valuable for ongoing performance and robustness,they do not inherently detect data drift-they respond to or stress-test existing issues. Applying overrides (option B) is a human-in-the-loop safeguard, not a method for drift detection.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Monitoring and Maintenance," Subsection: "Detection and Management of Data Drift"
NEW QUESTION # 79
A car rental company is developing an AI system to dynamically adjust rental pricing based on demand, location, and customer profiles. Which of the following is the MOST important reason to conduct specific testing during development?
Answer: A
Explanation:
Dynamic pricing algorithms can unintentionally discriminate against protected groups if trained on biased data or poorly designed features. The AAIA highlights fairness testing as a mandatory requirement in any AI solution that impacts customers financially or socially.
Specific ethical tests are needed to ensure:
Pricing does not vary unfairly based on demographics
Sensitive attributes ( ethnicity, age, gender ) are not inferred or misused Customer segmentation does not disproportionately disadvantage protected groups Historical biases do not propagate into automated pricing
NEW QUESTION # 80
An IS auditor has discovered that an organization ' s AI system is less accurate than the required threshold and recommends management implement cross-referencing multiple models in order to produce more accurate results. This is an example of which type of risk response?
Answer: D
Explanation:
Risk mitigation involves implementing controls or actions to reduce the likelihood or impact of a risk to an acceptable level. In this scenario, the model ' s failure to meet accuracy thresholds poses a performance and operational risk. By recommending the use of model ensembles or cross-referencing (where multiple models evaluate the same input), the auditor is proposing a technical control designed to improve the reliability of the system ' s output. This reduces the risk of incorrect decisions without eliminating the system entirely (Avoidance) or moving the risk to a third party (Transfer). Mitigation is the standard response for improving AI performance while maintaining the existing use case.
NEW QUESTION # 81
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