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IAPP Exam AIGP Topic 3 Question 9 Discussion

Actual exam question for IAPP's AIGP exam
Question #: 9
Topic #: 3
[All AIGP Questions]

CASE STUDY

Please use the following answer the next question:

A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.

The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.

The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.

The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.

During the procurement process, what is the most likely reason that the third-party consultant asked each vendor for information about the diversity of their datasets?

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Suggested Answer: B

The third-party consultant asked each vendor for information about the diversity of their datasets to assist in ensuring the fairness of the AI system. Diverse datasets help prevent biases and ensure that the AI system performs equitably across different demographic groups. This is crucial for a law enforcement application, where fairness and avoiding discriminatory practices are of paramount importance. Ensuring diversity in training data helps in building a more just and unbiased AI system. Reference: AIGP Body of Knowledge on Ethical AI and Fairness.


Contribute your Thoughts:

Xenia
3 months ago
Explainability? That's like asking a black box to explain its own workings. The consultant was wise to look at the diversity of the data.
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Gary
2 months ago
User 2
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Yuriko
3 months ago
User 1
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Weldon
3 months ago
Hmm, I'm not sure if the consultant's intention was to comply with the law. Seems like they were more focused on the system's reliability and fairness.
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Audry
2 months ago
Exactly, it's important to evaluate the reliability of the system to ensure accurate results.
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Mabel
2 months ago
Yeah, it makes sense. Having diverse training data can help prevent biases in the system.
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Kiley
2 months ago
I think the consultant asked about the diversity of datasets to ensure the fairness of the Al system.
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Adolph
3 months ago
So maybe the consultant asked about diversity to evaluate both fairness and reliability.
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Sylvia
4 months ago
The diversity of the dataset is crucial to ensure the fairness of the AI system. It's a smart move by the consultant to evaluate this aspect.
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Alethea
2 months ago
Definitely, it shows they were thorough in evaluating the vendors.
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Sharen
3 months ago
The consultant made a good call by considering this aspect during the procurement process.
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Karol
3 months ago
Yes, it helps prevent bias and ensures accurate results.
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Stephane
3 months ago
It's important to have diverse training data for fairness in the AI system.
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Honey
4 months ago
That's a good point, Wenona. Diversity could also affect the reliability of the system.
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Wenona
4 months ago
But wouldn't diversity also impact the accuracy of the system?
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Adolph
4 months ago
I agree with Honey. Diversity in training data can help prevent bias in the Al system.
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Honey
4 months ago
I think the consultant asked about diversity of datasets to ensure fairness.
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