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IAPP Exam AIGP Topic 2 Question 5 Discussion

Actual exam question for IAPP's AIGP exam
Question #: 5
Topic #: 2
[All AIGP Questions]

You are the chief privacy officer of a medical research company that would like to collect and use sensitive data about cancer patients, such as their names, addresses, race and ethnic origin, medical histories, insurance claims, pharmaceutical prescriptions, eating and drinking habits and physical activity.

The company will use this sensitive data to build an Al algorithm that will spot common attributes that will help predict if seemingly healthy people are more likely to get cancer. However, the company is unable to obtain consent from enough patients to sufficiently collect the minimum data to train its model.

Which of the following solutions would most efficiently balance privacy concerns with the lack of available data during the testing phase?

Show Suggested Answer Hide Answer
Suggested Answer: C

Utilizing synthetic data to offset the lack of patient data is an efficient solution that balances privacy concerns with the need for sufficient data to train the model. Synthetic data can be generated to simulate real patient data while avoiding the privacy issues associated with using actual patient data. This approach allows for the development and testing of the AI algorithm without compromising patient privacy, and it can be refined with real data as it becomes available. Reference: AIGP Body of Knowledge on Data Privacy and AI Model Training.


Contribute your Thoughts:

Curtis
4 months ago
Hey, at least they're not asking us to just deploy the current model and 'recalibrate it over time'! That would be like playing cancer prediction roulette.
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Chara
2 months ago
B) Extend the model to multi-modal ingestion with text and images.
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Tamala
2 months ago
C) Utilize synthetic data to offset the lack of patient data.
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Deeann
2 months ago
A) Deploy the current model and recalibrate it over time with more data.
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Rolland
2 months ago
That's a good point, using synthetic data could help balance privacy concerns while still training the model effectively.
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Alva
3 months ago
C) Utilize synthetic data to offset the lack of patient data.
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Garry
3 months ago
A) Deploy the current model and recalibrate it over time with more data.
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Carissa
4 months ago
Refocusing the algorithm to patients without cancer is an interesting idea, but I'm not sure it would give us the insights we need to predict cancer risk effectively.
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Audria
3 months ago
C) Utilize synthetic data to offset the lack of patient data.
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Johanna
3 months ago
A) Deploy the current model and recalibrate it over time with more data.
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Cyril
4 months ago
I'm not sure extending the model to multi-modal ingestion is the best idea. That could just end up making the privacy concerns even worse.
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Jani
3 months ago
D) Refocus the algorithm to patients without cancer.
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Carol
3 months ago
I agree, using synthetic data could be a good solution to balance privacy concerns.
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Amie
3 months ago
C) Utilize synthetic data to offset the lack of patient data.
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Nu
4 months ago
A) Deploy the current model and recalibrate it over time with more data.
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Hubert
4 months ago
I see your point, Joye, but I think option D could also be a viable solution.
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Estrella
4 months ago
Using synthetic data to offset the lack of patient data seems like the most ethical solution here. It allows us to build the model without compromising patient privacy.
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Geraldine
3 months ago
I agree. It's important to prioritize patient privacy while still being able to develop the algorithm.
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Lanie
4 months ago
That's a good point. Synthetic data could definitely help us move forward with the model.
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Joye
4 months ago
I disagree, I believe option A is more efficient.
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Lashanda
4 months ago
I think option C is the best solution.
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