When reviewing an organization's AI data governance program, which of the following is MOST important to validate to ensure compliance with privacy regulations?
Answer(s): A
Validating the implementation of privacy techniques in AI models is most important to ensure compliance with privacy regulations. Techniques such as anonymization, differential privacy, or data minimization directly safeguard personal data during AI processing.
An organization passes transaction data to an AI system so it can produce recommendations for customers. Which of the following is MOST important for management to do to keep this reliable?
Answer(s): D
Ensuring risk mitigation scenarios reduce exposures to a tolerable range is most important for keeping AI recommendations reliable. This approach manages operational, compliance, and reputational risks associated with AI outputs, maintaining trust and business continuity.
A banking organization uses AI to support the credit scoring process. Which of the following is the GREATEST benefit of providing interpretations of AI model outputs?
Answer(s): C
The greatest benefit of providing interpretations of AI model outputs is that it allows stakeholders to understand the reasons behind the outputs. This improves transparency, accountability, and trust in the credit scoring process.
When evaluating an AI system used to approve loan applications, an IS auditor notes decisions are made by the AI tool without oversight. Which of the following is the auditor's BEST recommendation?
Establishing early human review checkpoints is the best recommendation because it ensures oversight to detect errors or biases in AI-driven loan approvals. This protects fairness, accountability, and compliance with regulatory requirements.
When evaluating whether an AI algorithm deployed by an organization aligns with its business objectives, it is MOST important to:
Answer(s): B
Confirming that the outcomes from using the algorithm support strategic goals is most important. This ensures the AI deployment aligns with business objectives, delivering value while maintaining relevance to organizational priorities.
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