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| Section | Objectives |
|---|---|
| AI Auditing Tools and Techniques | |
| AI Governance and Risk | |
| AI Operations |
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NEW QUESTION # 113
An IS auditor is planning an audit covering a vendor-provided and supported AI model used by the organization to predict train track maintenance. Which of the following is the BEST approach for the IS auditor to test the model?
Answer: A
Explanation:
When auditing vendor-provided (third-party) AI models, the organization often lacks access to the underlying source code, neural network architecture, or weights. In such cases, the AAIA™ manual recommends " Black Box Testing " as the most practical and effective approach. This involves testing the model based solely on its inputs and outputs to verify its predictive accuracy, fairness, and reliability without needing internal technical details. White box testing (Option B) and code reviews (Option D) are typically unfeasible due to proprietary restrictions. Black box testing allows the auditor to validate that the model meets business requirements and operates within acceptable risk tolerances as defined in the vendor agreement.
NEW QUESTION # 114
Which metric should an IS auditor review to evaluate issues with data collection that could impact AI model training?
Answer: B
Explanation:
The percentage of missing values (option B) directly reflects data collection issues. Missing or incomplete data can degrade model performance, distort feature distributions, and create biased or inaccurate predictions.
AAIA stresses that auditors must evaluate:
Completeness
Validity
Accuracy
Consistency
Missing values signal failures in upstream processes, including sensors, user inputs, integrations, or data pipelines.
NEW QUESTION # 115
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?
Answer: A
Explanation:
Evasion attacks occur when an attacker modifies input data (such as adding subtle noise to an image) to trick a model into misclassification. The AAIATM manual identifies "Adversarial Training" as a primary defense, where the model is intentionally exposed to adversarial examples during the training phase to improve its robustness and resilience. This allows the model to learn the patterns associated with malicious inputs.
NEW QUESTION # 116
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: A
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 AAIATM 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 # 117
Which control is MOST effective in reducing the risk of sensitive data being exposed through a large language model's outputs?
Answer: B
Explanation:
Limiting what sensitive data enters the model (minimization) and filtering outputs before they reach end users reduces the risk of inadvertent disclosure of PII or confidential data.
NEW QUESTION # 118
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