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NEW QUESTION # 148
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
Answer: D
Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.
References:
AAISM Study Guide - AI Risk Management (Bias and Fairness in AI)
ISACA AI Security Management - Data Diversity and Representation Controls
NEW QUESTION # 149
Which of the following is the PRIMARY benefit of incorporating an AI component into an organization's security program?
Answer: D
Explanation:
Incorporating an AI component ensures that security efforts address AI-specific threats and vulnerabilities while aligning with established cybersecurity practices, strengthening the overall security posture.
NEW QUESTION # 150
An internet service provider (ISP) uses a chatbot based on a public model to help resolve connectivity issues. Customers report that the chatbot's instructions lack details about the ISP's proprietary equipment. Which of the following is the BEST way to improve the user experience?
Answer: D
Explanation:
Retrieval-augmented generation integrates the chatbot with the organization's proprietary knowledge sources, enabling it to provide accurate and detailed responses specific to the ISP's equipment while maintaining the strengths of the base model.
NEW QUESTION # 151
Which of the following BEST describes the role of transparency in AI?
Answer: B
Explanation:
Transparency in AI is a governance principle requiring that systems be explainable to stakeholders in ways that are understandable and meaningful, enabling clear articulation of how decisions were reached and why.
Within an AI program, transparency supports accountability, auditability, and trust by ensuring that reasons for decisions can be communicated and scrutinized. Option C reflects this definition by focusing on intelligible, logical explanations of system behavior and decision rationale.
Option A is a narrow technique (model-specific interpretability for decision trees) and does not capture transparency as a broad governance requirement. Option B conflates transparency with full public disclosure; transparency does not require making all artifacts openly available. Option D is persuasion/advocacy, not transparency.
References: AI Security Management™ (AAISM) Body of Knowledge: "AI Governance-Transparency and Explainability," "Accountability and Assurance"; AAISM Study Guide: "Explainability Objectives and Stakeholder Communication," "Documentation for Decision Rationale."
NEW QUESTION # 152
Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI's access to and use of internet data?
Answer: B
Explanation:
The AAISM materials highlight that one of the primary legal risks with generative AI systems is the unauthorized use of copyrighted or intellectual property-protected data drawn from internet sources. To mitigate lawsuits, the most effective recommendation is to implement filtering logic that actively excludes data flagged for intellectual property risks before ingestion or generation.
While disclosing compliance policies, appointing governance roles, or reviewing logs are supportive measures, they do not directly prevent the core liability of using restricted content. The study guide explicitly emphasizes that proactive filtering and data governance controls are the most effective safeguards against legal disputes concerning content origin.
NEW QUESTION # 153
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