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NEW QUESTION # 210
When deriving statistical information from AI systems, which source of risk is MOST important to address?
Answer: A
Explanation:
AAISM emphasizes systemic or structural bias as a high-impact risk because biased data leads directly to discriminatory insights or decisions when used for analytics or reporting. This risk affects fairness, compliance, and organizational reputation.
Hallucinations (A) relate more to generative AI. Incomplete outputs (B) affect accuracy but not structural fairness. Lack of normalization (C) affects performance but is not the dominant risk.
References: AAISM Study Guide - AI Bias, Fairness, and Ethical Risk.
NEW QUESTION # 211
The MOST important consideration when establishing key risk indicators (KRIs) to monitor AI model drift in an operational machine learning (ML) system is adopting KRIs that:
Answer: B
Explanation:
AI model drift occurs when a model's performance or behavior changes over time due to evolving data patterns or environments. KRIs that identify variations in model accuracy and outputs are the most important because they directly indicate whether the model is deviating from expected performance and reliability.
NEW QUESTION # 212
An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs.
Which of the following biases does this scenario demonstrate?
Answer: D
Explanation:
AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation.
Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.
NEW QUESTION # 213
The PRIMARY AI governance risk of inconsistently applying data augmentation and cleansing across datasets is that the model may:
Answer: D
Explanation:
Inconsistent data augmentation and cleansing can introduce or amplify bias within datasets, resulting in unreliable or unfair model behavior. This undermines both data integrity and model integrity, creating significant governance, ethical, and operational risks.
NEW QUESTION # 214
A financial organization uses AI to detect potential fraudulent activities but is concerned about the impact of potential data poisoning. Which of the following controls would BEST mitigate this risk?
Answer: B
Explanation:
AAISM identifies training-data diversity and provenance assurance as primary treatments against data poisoning. Sourcing data from multiple, independently governed providers, combined with ingestion validation and anomaly screening, reduces the chance that a single compromised source can skew model behavior and improves cross-source consistency checks. Transparency, break-glass, and awareness are valuable but do not directly reduce poisoning exposure at the training boundary.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Governance & Integrity for AI; Adversarial ML: Poisoning Threats and Mitigations; Supplier and Source Diversification Controls.
NEW QUESTION # 215
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