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Topic 1: Part 1
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?
A. Data architects.
B. Government regulators.
C. Socio-cultural and technical experts.
D. Legal and privacy governance experts.
Explanation: Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects). Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry. Reference: AIGP BODY OF KNOWLEDGE and AI development frameworks.
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?
A. Fines for SMEs and startups will be proportionally capped.
B. Rules on General Purpose Al will apply after 6 months as a specific provision.
C. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.
D. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
Explanation: The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.
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?
A. Retrain the model with data that reflects demographic parity.
B. Procure a third-party statistical bias assessment tool.
C. Document all instances of bias in the data set.
D. Delete all gender-based data in the data set.
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.
An EU bank intends to launch a multi-modal Al platform for customer engagement and
automated decision-making assist with the opening of bank accounts. The platform has
been subject to thorough risk assessments and testing, where it proves to be effective in
not discriminating against any individual on the basis of a protected class.
What additional obligations must the bank fulfill prior to deployment?
A. The bank must obtain explicit consent from users under the privacy Directive.
B. The bank must disclose how the Al system works under the Ell Digital Services Act.
C. The bank must subject the Al system an adequacy decision and publish its appropriate safeguards.
D. The bank must disclose the use of the Al system and implement suitable measures for users to contest automated decision-making.
Explanation: Under the EU regulations, particularly the GDPR, banks using AI for decision-making must inform users about the use of AI and provide mechanisms for users to contest decisions. This is part of ensuring transparency and accountability in automated processing. Explicit consent under the privacy directive (A) and disclosing under the Digital Services Act (B) are not specifically required in this context. An adequacy decision is related to data transfers outside the EU (C).
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.
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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