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IAPP Exam AIGP Topic 1 Question 29 Discussion

Actual exam question for IAPP's AIGP exam
Question #: 29
Topic #: 1
[All AIGP Questions]

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 .. 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.

During the first month when ABC monitors the model for bias, it is most important to?

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Suggested Answer: A

During the first month of monitoring the model for bias, it is most important to continue disparity testing. Disparity testing involves regularly evaluating the model's decisions to identify and address any biases, ensuring that the model operates fairly across different demographic groups.


Contribute your Thoughts:

Mitsue
11 days ago
We should also compare the results to human decisions for validation.
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Glory
15 days ago
Hah, approval from management? Good luck with that. They're probably the ones who introduced the bias in the first place!
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Janine
4 days ago
C: Compare the results to human decisions prior to deployment.
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Marylin
6 days ago
B: Analyze the quality of the training and testing data.
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Maryann
8 days ago
A: Continue disparity testing.
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Veronika
20 days ago
I agree with Hoa, it's crucial to monitor for bias in the model.
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Franchesca
22 days ago
I'd go with C. Comparing to human decisions is key, but don't forget to cover your you-know-what and get management approval too.
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Margery
7 days ago
D) Seek approval from management for any changes to the model.
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Yvonne
10 days ago
C) Compare the results to human decisions prior to deployment.
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Hoa
1 months ago
I think we should continue disparity testing to ensure fairness.
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Jeannetta
1 months ago
Comparing the results to human decisions is crucial. That'll show if the model is actually an improvement or just perpetuating existing biases.
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Dannie
1 months ago
Definitely need to analyze the quality of the training and testing data. If there's bias in the historical data, the model will just reflect that. Gotta get to the root of the issue.
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Buffy
4 days ago
Analyzing the data will help address any issues and improve the model's accuracy.
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Ria
10 days ago
We need to ensure the model is not perpetuating any existing biases.
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Davida
20 days ago
We should also consider comparing the results to human decisions to see where the model may be deviating.
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Effie
24 days ago
It's important to make sure the data used is accurate and unbiased.
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Irma
26 days ago
Agreed, analyzing the quality of the training and testing data is crucial.
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Minna
27 days ago
It's important to ensure that the data used to train the model is representative and unbiased.
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Walker
1 months ago
Agreed, analyzing the quality of the training and testing data is crucial to address bias in the model.
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