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| Section | Objectives |
|---|---|
| Topic 1: AI Governance and Risk | |
| Topic 2: AI Operations | |
| Topic 3: AI Auditing Tools and Techniques |
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NEW QUESTION # 308
Which of the following is the BEST way to support the development and design of high-risk AI systems?
Answer: D
Explanation:
The AAIA™ Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.
"Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high- risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance." While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase.
Therefore, option C is the most effective practice.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Governance and Management"
NEW QUESTION # 309
An organization ' s AI model used in the recruitment screening process was found to favor one gender of candidates over others. It was noted that the AI model was trained on recruitment records where the majority of new hires were male. Which bias would this BEST demonstrate?
Answer: B
Explanation:
" Historical Bias " occurs when the training data reflects existing social prejudices or past inequalities. If an organization's historical hiring practices favored males, an AI model trained on those records will " learn " that being male is a characteristic of a successful candidate. This automates and perpetuates past discrimination into future decisions. The AAIA™ manual emphasizes that auditors must evaluate training data for historical bias to ensure fairness. Unlike confirmation bias (which involves seeking data that supports existing beliefs) or label bias (which involves incorrect classification), historical bias is a systemic issue inherent in the data collection from a flawed real-world environment.
NEW QUESTION # 310
The BEST way to prevent sensitive information disclosure by large language model (LLM) chatbots is through:
Answer: C
Explanation:
Large Language Models (LLMs) have the capacity to memorize and unintentionally reveal sensitive information if not properly managed. As per the AAIATM Study Guide, data masking is a critical technique that prevents the exposure of personally identifiable information (PII) or confidential content by obscuring or replacing sensitive parts of the data during training or interaction.
"Data masking ensures that training data used for LLMs does not contain real sensitive identifiers. Unlike sanitization, masking modifies data to maintain utility while eliminating exposure risk." Manual monitoring and access controls are supportive security measures, and data sanitization helps remove content but may not preserve the data's structure. Data masking offers the most proactive and technically robust solution.
NEW QUESTION # 311
An AI social media platform uses an algorithm to increase user engagement that could unintentionally promote divisive content. Which of the following is the BEST course of action to mitigate this risk?
Answer: A
NEW QUESTION # 312
An organization is using a large language model (LLM) to assist in evaluating loan applications, but the training data used is known to be incomplete. Which of the following is the GREATEST associated risk?
Answer: D
Explanation:
Incomplete training dataoften leads to underrepresentation of certain applicant types, products, or scenarios.
In credit and lending, this typically translates intosystematic bias: some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is thereforeunfair loan decisions(A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
While delays (B), reduced satisfaction (C), or increased manual work (D) may occur, they are secondary operational issues. AAIA highlights that for financial services, the central risks includefairness, discrimination, regulatory compliance, and reputational impact. Incomplete data directly undermines fairness and can violate lending regulations and internal risk appetite.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (risk categories, including fairness and discriminatory outcomes).
ISACA AI ethics content on data completeness and representativeness in decisioning systems.
NEW QUESTION # 313
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