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NEW QUESTION # 197
Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?
Answer: C
Explanation:
Ethical risk begins with the nature of the data being used. The sensitivity and origin of training data (option B) determine whether the model risks violating privacy, perpetuating historical bias, or using data that was collected without proper consent.
AAIA identifies data provenance, data sensitivity, and lawfulness of processing as central to ethical AI. Training data that contains sensitive attributes (e.g., health, ethnicity, gender, financial status) must be reviewed carefully for compliance and ethical impacts.
NEW QUESTION # 198
An IS auditor analyzed an AI model scorecard and identified that training data was imbalanced.
Which of the following is the BEST recommendation to remediate risk?
Answer: A
Explanation:
Data imbalance (where one class significantly outweighs another) leads to biased models that perform poorly on minority classes. The ISACA AAIATM manual suggests that "class-weighted loss" is a proactive technical control that penalizes the model more for misclassifying minority samples, effectively "balancing" the learning process. Stratified splitting ensures that every training and validation set maintains the same ratio of classes as the original data, preventing the model from failing to "see" minority cases during the testing phase.
NEW QUESTION # 199
Which of the following is the PRIMARY purpose of an AI acceptable use policy?
Answer: A
Explanation:
An AI acceptable use policy (AUP) defines how AI tools and technologies should be ethically and responsibly used within an organization. According to the AAIATM Study Guide, the primary goal of an AUP is to prevent misuse and promote adherence to ethical, legal, and operational standards.
"An AI acceptable use policy provides governance over how AI tools may be used, especially regarding data handling, fairness, and prohibited uses. It aligns employee actions with organizational values and compliance requirements."
NEW QUESTION # 200
Which of the following is the BEST way to ensure an AI model ' s outputs are effectively controlled for bias?
Answer: D
Explanation:
The most objective evidence of bias control is " Performance Parity " . If the accuracy, precision, and recall are " Similar across various demographic groups, " it demonstrates that the model is functioning equitably for all users. While having a policy definition (Option B) is good governance, it doesn ' t prove the model is fair in practice. Restricting training to " Real-world human decisions " (Option C) often introduces bias, as it simply automates historical human discrimination. Transparency (Option D) is necessary for auditing but does not, on its own, prevent a model from being biased.
NEW QUESTION # 201
An IS auditor notes that an AI model achieved significantly better results on training data than on test data. Which of the following problems with the model has the IS auditor identified?
Answer: D
Explanation:
Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIATM Study Guide identifies overfitting as a common problem that impacts model reliability.
"Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs."
NEW QUESTION # 202
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