Question # 1
| Which of the following elements of feature engineering is most important to mitigate the
potential bias in an Al system? | | A. Feature selection. | | B. Feature validation. | | C. Feature transformation. | | D. Feature importance analysis. |
A. Feature selection.
Explanation:
Feature selection is the most important element of feature engineering to mitigate potential
bias in an AI system. This process involves choosing the most relevant and representative
features from the data set, which directly affects the model’s performance and fairness. By
carefully selecting features, data scientists can reduce the influence of biased or irrelevant
attributes, ensuring that the AI system is more accurate and equitable. Proper feature
selection helps in eliminating biases that might stem from socio-demographic factors or
other sensitive variables, leading to a more balanced and fair AI model.
Question # 2
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? | | A. A transparency notice. | | B. An opt-out mechanism. | | C. Detailed terms of service. | | D. Customer service support. |
A. A transparency notice.
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.
Question # 3
| What is the main purpose of accountability structures under the Govern function of the
NIST Al Risk Management Framework? | | A. To empower and train appropriate cross-functional teams. | | B. To establish diverse, equitable and inclusive processes. | | C. To determine responsibility for allocating budgetary resources. | | D. To enable and encourage participation by external stakeholders. |
A. To empower and train appropriate cross-functional teams.
Explanation:
The NIST AI Risk Management Framework’s Govern function emphasizes the importance
of establishing accountability structures that empower and train cross-functional teams.
This is crucial because cross-functional teams bring diverse perspectives and expertise,
which are essential for effective AI governance and risk management. Training these
teams ensures that they are well-equipped to handle their responsibilities and can make
informed decisions that align with the organization’s AI principles and ethical standards.
Question # 4
CASE STUDY
Please use the following 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 Al to create the
enrichment courses, and that many of the students are using generative Al 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 Al 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 Al, including by teachers and students, going forward.
What is the best reason for GVC to offer students the choice to utilize generative Al in
limited, defined circumstances? | | A. Toenable students to learn how to manage their time. | | B. Toenable students to learn about performing research. | | C. Toenable students to learn about practical applications of Al. | | D. Toenable students to learn how to use Al as a supportive
educational tool. |
D. Toenable students to learn how to use Al as a supportive
educational tool.
Question # 5
| Under the NIST Al Risk Management Framework, all of the following are defined as
characteristics of trustworthy Al EXCEPT? | | A. Tested and Effective. | | B. Secure and Resilient. | | C. Explainable and Interpretable. | | D. Accountable and Transparent. |
A. Tested and Effective.
Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy
AI, including being secure and resilient, explainable and interpretable, and accountable and
transparent. While being tested and effective is important, it is not explicitly listed as a
characteristic of trustworthy AI in the NIST framework. The focus is more on the system's
ability to function safely, securely, and transparently in a way that stakeholders can
understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.
Question # 6
| What is the primary purpose of an Al impact assessment? | | A. To define and evaluate the legal risks associated with developing an Al system. | | B. Anticipate and manage the potential risks and harms of an Al system. | | C. To define and document the roles and responsibilities of Al stakeholders. | | D. To identify and measure the benefits of an Al system. |
B. Anticipate and manage the potential risks and harms of an Al system.
Question # 7
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.
Which of the following is the most important reason to train the underwriters on the model
prior to deployment? | | A. Toprovide a reminder of a right appeal. | | B. Tosolicit on-going feedback on model performance. | | C. Toapply their own judgment to the initial assessment. | | D. Toensure they provide transparency applicants on the model. |
C. Toapply their own judgment to the initial assessment.
Explanation:
Training underwriters on the model prior to deployment is crucial so they can apply their
own judgment to the initial assessment. While AI models can streamline the process,
human judgment is still essential to catch nuances that the model might miss or to account
for any biases or errors in the model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight
in AI systems, particularly in high-stakes areas such as underwriting and loan approvals.
Human underwriters can provide a critical review and ensure that the model's assessments
are accurate and fair, integrating their expertise and understanding of complex cases.
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