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| Section | Objectives |
|---|---|
| AI Lifecycle Governance Implementation | - Operational governance controls
|
| AI Risk Management and Impact Assessment | - Risk identification and mitigation
|
| Regulatory and Legal Frameworks for AI | - Global AI governance regulations
|
| Foundations of AI Systems and Governance | - Governance principles and accountability
|
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NEW QUESTION # 146
CASE STUDY
Please use the following to answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative AI to create the enrichment courses, and that many of the students are using generative AI to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students.
GVC has also discovered that an art teacher has expressly incorporated the use of generative AI into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative AI, including by teachers and students, going forward.
All of the following may be copyright risks from teachers using generative AI to create course content EXCEPT:
Answer: B
Explanation:
Student consent is not typically a copyright risk related to teachers using generative AI; the other options highlight key copyright concerns around AI-generated content and data training sources.
NEW QUESTION # 147
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 a 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.
What is the best strategy to mitigate the bias uncovered in the loan applications?
Answer: D
Explanation:
Retraining the model with data that reflects demographic parity is the best strategy to mitigate the bias uncovered in the loan applications. This approach addresses the root cause of the bias by ensuring that the training data is representative and balanced, leading to more equitable decision-making by the AI model.
Reference: The AIGP Body of Knowledge stresses the importance of using high-quality, unbiased training data to develop fair and reliable AI systems. Retraining the model with balanced data helps correct biases that arise from historical inequalities, ensuring that the AI system makes decisions based on equitable criteria.
NEW QUESTION # 148
CASE STUDY
Please use the following to answer the next question:
A premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
To address these concerns, the company is considering using a third-party AI tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party AI-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions. One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by AI hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the AI hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
Which other stakeholder groups should be involved in the selection and implementation of the AI hiring tool?
Answer: A
Explanation:
Finance and Legal teams are critical stakeholders in selecting and implementing AI hiring tools to ensure budget compliance and adherence to legal and regulatory requirements.
NEW QUESTION # 149
What is the best method to apply to an AI model so that it is statistically unlikely that a specific piece of training data can be identified from a model ' s output?
Answer: C
Explanation:
The correct answer is C because differential privacy is specifically designed to ensure that the inclusion or exclusion of any single data point does not significantly affect the output of a model. This provides strong mathematical guarantees that individual training data cannot be reverse-engineered or identified from model outputs. Differential privacy works by introducing controlled noise into the data or model outputs, thereby protecting individual-level information while preserving overall statistical patterns. In AI governance, it is widely recognized as a key privacy-enhancing technology for mitigating risks such as data leakage, membership inference attacks, and re-identification. Other options, such as homomorphic encryption, focus on secure computation, while data sharding and compartmentalization address data management but do not provide formal privacy guarantees at the model output level.
NEW QUESTION # 150
Which of the following use cases would be best served by a non-AI solution?
Answer: B
Explanation:
Developing a social media presence for a non-profit is best served by non-AI solutions. This task primarily involves content creation, community engagement, and strategic planning, which are effectively managed by human expertise and traditional marketing tools. AI is more suitable for tasks requiring automation, large-scale data analysis, and personalized recommendations, such as e-commerce personalization, forecasting cost overruns, or automating customer service responses. Reference: AIGP Body of Knowledge on AI Use Cases and Applications.
NEW QUESTION # 151
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