IAPP AIGP Exam - 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?

AContinue disparity testing.

BAnalyze the quality of the training and testing data.

CCompare the results to human decisions prior to deployment.

DSeek approval from management for any changes to the model.

Show Suggested Answer

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.

by Tiffiny at Jun 13, 2025, 07:36 AM

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Hobert

3 months ago

Not sure if seeking management approval will solve the bias issue.

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Boris

3 months ago

Wait, are they really using historical data that could be biased?

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Alpha

3 months ago

Sounds like a classic case of bias in AI.

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Quentin

3 months ago

Definitely need to compare results to human decisions first!

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France

3 months ago

I think analyzing the training data is crucial here.

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Kathrine

4 months ago

I feel like seeking management approval for changes could be important, but it seems like we should focus on the model's performance first.

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Samira

4 months ago

I'm leaning towards continuing disparity testing since we need to address the bias issue quickly, but I'm not completely confident.

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Gene

4 months ago

I think we practiced a similar question where we had to evaluate model outputs against human decisions. That might be relevant here too.

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Elmer

4 months ago

I remember we discussed the importance of analyzing training data quality in class, but I'm not sure if that's the most critical step right now.

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Malcolm

5 months ago

Hmm, I'm not sure seeking approval from management is the right move here. We need to be proactive in fixing this issue before it causes any further harm.

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Leatha

5 months ago

I feel pretty confident about this one. Comparing the model's decisions to the previous human decisions seems like the most straightforward way to identify and address the bias.

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Rosendo

5 months ago

I'm a bit confused on the best approach here. Should we just continue testing for disparities, or is there something more proactive we can do to address the issue?

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Berry

5 months ago

Okay, I think the key here is to focus on the quality and fairness of the training data. We need to make sure there aren't any inherent biases or imbalances that are getting reflected in the model's decisions.

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Krystina

5 months ago

Hmm, this seems like a tricky one. I'll need to carefully analyze the data and model to identify the source of the bias against women's applications.

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Mitsue

8 months ago

We should also compare the results to human decisions for validation.

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Glory

8 months 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

8 months ago

C: Compare the results to human decisions prior to deployment.

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Marylin

8 months ago

B: Analyze the quality of the training and testing data.

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Maryann

8 months ago

A: Continue disparity testing.

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Veronika

8 months ago

I agree with Hoa, it's crucial to monitor for bias in the model.

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Franchesca

9 months 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

8 months ago

D) Seek approval from management for any changes to the model.

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Yvonne

8 months ago

C) Compare the results to human decisions prior to deployment.

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Hoa

9 months ago

I think we should continue disparity testing to ensure fairness.

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Jeannetta

9 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

9 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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