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NEW QUESTION # 262
Which of the following pre-processing steps would MOST effectively justify an AI model's decision to a non-technical stakeholder?
Answer: D
Explanation:
While all the listed techniques (except penetration testing) support interpretability, "LIME" is specifically noted in the ISACA AAIATM Study Guide for its ability to explain individual decisions.
LIME creates a simpler, interpretable model around a specific prediction to show which features (e.g., high income or low debt) were the primary drivers for that specific case.
NEW QUESTION # 263
An organization is conducting an audit of an AI decision-making system being used for talent recruitment.
Which of the following is MOST critical to evaluate in order to ensure the system meets stakeholder needs?
Answer: A
Explanation:
While predictive accuracy and timeliness are operational requirements, " Decision fairness " is the most critical ethical and legal consideration in recruitment AI. Stakeholders, including candidates, regulators, and management, require assurance that the system does not discriminate against protected groups based on gender, age, or ethnicity. The ISACA AAIA™ Study Guide emphasizes that for high-impact human resource applications, auditors must prioritize fairness testing and bias assessments to prevent reputational damage and legal liability. A system that is highly accurate but biased fails to meet the ethical standards and " stakeholder needs " of modern governance.
NEW QUESTION # 264
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?
Answer: B
Explanation:
The use of customer data in AI training presents a significant privacy risk, especially when the data is not properly anonymized or when consent has not been explicitly obtained. The AAIA™ Study Guide classifies the unauthorized or inadvertent disclosure of personally identifiable information (PII) as the most severe risk in such contexts.
"Training AI models on customer data without proper safeguards can result in the unintentional exposure of personal or sensitive information, leading to data breaches and compliance violations." While bias (B) and hallucinations (D) are important operational concerns, they do not pose the same regulatory and ethical severity as PII exposure. Therefore, A is the most critical risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy Risks and Data Minimization"
NEW QUESTION # 265
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 # 266
Which of the following is the MOST important AI data governance method to protect privacy and data security?
Answer: A
Explanation:
Periodic monitoring is the MOST critical governance mechanism for protecting privacy and data security in AI systems, especially those handling sensitive or personal data. ISACA's AAIA guidance emphasizes that data governance must be continuous because AI systems evolve, data drifts, risk exposures shift, and privacy threats increase over time.
Periodic monitoring ensures:
Detection of unauthorized access
Identification of anomalous data use
Confirmation that privacy controls remain effective
Early detection of data misclassification or leakage
Verification of compliance with retention, deletion, and minimization requirements
NEW QUESTION # 267
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