A financial services company is developing AI models to predict customer credit risk. To reduce privacy concerns, the company decides to train the models primarily using synthetic data. Which of the following is an IS auditor's BEST recommendation to evaluate synthetic data risk?
Answer(s): C
Requiring validation of synthetic data quality is the best recommendation, as poor-quality or unrepresentative synthetic data can lead to inaccurate or biased credit risk predictions. Validation ensures the synthetic data reliably reflects real-world patterns while protecting privacy.
It was determined that an AI system used for medical diagnoses shows decreased accuracy for minority populations. Which of the following is the BEST way to address this issue?
Answer(s): A
Retraining the model with more representative data is the best way to address decreased accuracy for minority populations. Ensuring diverse and inclusive training data reduces bias and improves fairness in medical diagnoses.
An IS auditor has identified that the configuration of a proprietary AI model has been leaked. Which of the following is the MOST significant risk?
Answer(s): D
The most significant risk is that attackers can exploit vulnerabilities in the model. With access to configuration details, adversaries can identify weaknesses and manipulate the AI system, leading to compromised integrity, security breaches, or malicious misuse.
An AI generated photo showing a catastrophe circulated on social media, impacting the supply chain. This is an example of which type of risk?
Answer(s): B
This is an example of societal risk, as the AI-generated photo spreads misinformation that influences public perception and disrupts social and economic stability, in this case causing supply chain impact.
Which of the following processes is MOST important for an organization to have in place to ensure the credibility of AI outputs?
Data governance is most important for ensuring the credibility of AI outputs because well-managed, accurate, and high-quality data directly impacts the reliability, fairness, and trustworthiness of AI model results.
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