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AIGP · Question #47
AIGP Question #47: Real Exam Question with Answer & Explanation
The correct answer is B. Providing the applicants with information about the model capabilities and limitations.. Providing applicants with information about model capabilities is important for transparency but does not directly support fairness testing, which focuses on evaluating and mitigating bias in decision-making.
Question
CASE STUDY Please use the following to answer the next question: A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). The company 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. The company 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. They have 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, the company realizes that the LLM declines a higher percentage of women's applications. Each of the following steps would support fairness testing by the compliance team during the first month in production EXCEPT:
Options
- AValidating a similar level of decision-making across different demographic groups.
- BProviding the applicants with information about the model capabilities and limitations.
- CIdentifying if additional training data should be collected for specific demographic groups.
- DUsing tools to help understand factors that may account for differences in decision-making.
Explanation
Providing applicants with information about model capabilities is important for transparency but does not directly support fairness testing, which focuses on evaluating and mitigating bias in decision-making.
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