Isaca ISACA Advanced in AI Audit (AAIA) AAIA Question # 1 Topic 1 Discussion
AAIA Exam Topic 1 Question 1 Discussion:
Question #: 1
Topic #: 1
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?
A.
The effectiveness of data anonymization processes that help preserve data quality
B.
The algorithms for generative AI models designed to detect and correct data bias before processing
C.
The frequency of data audits verifying the integrity and accuracy of inputs
D.
The measures in place to ensure the appropriateness and relevance of input data for generative AI models
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations—where generative models produce fabricated or misleading outputs. The AAIA™ Study Guide notes, “Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs.”
While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.
[Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Input Data Governance for Generative AI", ]
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