IAPP Artificial Intelligence Governance Professional (AIGP) — Question 56

CASE STUDY -
Please use the following to 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 to 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?

Answer options

• A. To provide a reminder of a right to appeal.
• B. To solicit on-going feedback on model performance.
• C. To apply their own judgment to the initial assessment.
• D. To ensure they provide transparency to applicants on the model.

Correct answer: C

Explanation

The correct answer is C because underwriters need to apply their expertise and judgment to the initial assessments made by the model, especially when biases are detected. Options A, B, and D are not as critical in this context; while they are important, they do not address the immediate need for human oversight and judgment in assessing applications that may be influenced by biased model outputs.