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NEW QUESTION # 44
CASE STUDY
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
When prioritizing the updates to its policies, rules and procedures to include the new AI system for user authentication, the organization should:
Answer: D
Explanation:
The correct answer is C. This action ties directly into principles of data minimization, purpose limitation, and lawfulness of processing, which are central to privacy and AI governance.
From the AIGP Body of Knowledge, Section on Privacy Considerations:
"Personal data must only be processed for specified and lawful purposes. Organizations must consider whether they have a legal basis for processing such data under data protection laws like the GDPR or CCPA." Additionally, AI Governance in Practice Report 2024 emphasizes:
"One of the most significant challenges when designing and developing AI systems is ensuring the data used is appropriate for the intended purpose... Managing unnecessary data, especially data that may contain sensitive attributes, can increase risk."
NEW QUESTION # 45
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed tA. human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
The best approach to enable a customer who wants information on the Al model's parameters for underwriting purposes is to provide?
Answer: B
Explanation:
The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide a transparency notice. This notice should explain the nature of the AI system, how it uses customer data, and the decision-making process it follows. Providing a transparency notice is crucial for maintaining trust and compliance with regulatory requirements regarding the transparency and accountability of AI systems.
Reference: According to the AIGP Body of Knowledge, transparency in AI systems is essential to ensure that stakeholders, including customers, understand how their data is being used and how decisions are made. This aligns with ethical principles of AI governance, ensuring that customers are informed and can make knowledgeable decisions regarding their interactions with AI systems.
NEW QUESTION # 46
Random forest algorithms are in what type of machine learning model?
Answer: A
Explanation:
Random forest algorithms are discriminative models because they focus on modeling the decision boundary between classes to classify input data.
NEW QUESTION # 47
Business A sells software that provides users with writing and grammar assistance. Business B is a cloud services provider that trains its own AI models.
* Business A has decided to add generative AI features to their software.
* Rather than create their own generative AI model, Business A has chosen to license a model from Business B:
* Business A will then integrate the model into their writing assistance software to provide generative AI capabilities.
* Business A is most concerned that its writing assistance software could recommend toxic or obscene text to its users.
Which of the following governance processes should Business A take to best protect its users against potentially inappropriate text?
Answer: B
Explanation:
Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.
According to theAI Governance in Practice Report 2024, organizations thatdeployAI must engage in performance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:
"Operational governance... development of: #Performance monitoring protocols to ensure systems perform adequately for their intended purposes." (p. 12)
"Product governance... includes: #System impact assessments to identify and address risk prior to product development or deployment." (p. 11) Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:
"The act imposes regulatory obligations... such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance..." (p. 7) Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A's minimum content safety requirements.
Let's now evaluate the incorrect options:
* A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.
* C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A's primary concern.
* D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A's content safety standards.
Thus,testing the model's behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.
NEW QUESTION # 48
Decreasing the complexity of a machine learning model reduces variance and?
Answer: A
Explanation:
The correct answer is A because of the fundamental bias-variance tradeoff in machine learning. When model complexity is reduced, the model becomes simpler and less flexible, which decreases variance because it is less sensitive to fluctuations in the training data. However, this simplification comes at the cost of increased bias, meaning the model may oversimplify relationships and fail to capture underlying patterns accurately.
This tradeoff is a core concept in AI fundamentals and directly impacts model per formance, reliability, and governance decisions. From an AI governance perspective, understanding this balance is critical when evaluating model risk, as high bias can lead to systematic errors and fairness issues, while high variance can result in instability and unpredictability in outputs. Proper model tuning aims to balance both for optimal and responsible performance.
NEW QUESTION # 49
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