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Salesforce Exam Salesforce AI Associate Topic 2 Question 25 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam
Question #: 25
Topic #: 2
[All Salesforce AI Associate Questions]

What is one technique to mitigate bias and ensure fairness in AI applications?

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Suggested Answer: A

A technique to mitigate bias and ensure fairness in AI applications is ongoing auditing and monitoring of the data used in AI applications. Regular audits help identify and address any biases that may exist in the data, ensuring that AI models function fairly and without prejudice. Monitoring involves continuously checking the performance of AI systems to safeguard against discriminatory outcomes. Salesforce emphasizes the importance of ethical AI practices, including transparency and fairness, which can be further explored through Salesforce's AI ethics guidelines at Salesforce AI Ethics.


by Silvana at Aug 08, 2024, 05:11 PM

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Karma
9 months ago
I believe using data with more examples of minority groups, as in option C, is crucial for fairness.
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Helene
9 months ago
I'm not sure, but I think excluding data features like in option B could also help.
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Erinn
9 months ago
I agree with Wilbert, because auditing can help identify and correct biases.
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Wilbert
10 months ago
I think the answer is A) Ongoing auditing and monitoring of data.
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Levi
10 months ago
As a data scientist, I can tell you that the only way to ensure true fairness is to include a diversity of perspectives. This exam question is like a riddle wrapped in an enigma, wrapped in a burrito. Delicious, but complex!
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Mattie
10 months ago
I think using data with more examples of minority groups, like option C, can help ensure fairness in AI applications.
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Dominque
10 months ago
Using data with more minority examples? That's an interesting approach, but I wonder if it might introduce its own set of biases. Tricky stuff, this AI fairness business.
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Malcolm
9 months ago
It's definitely a tricky balance to strike. We have to be careful with every approach we take.
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Laine
9 months ago
C) Using data that contains more examples of minority groups than majority groups
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Erick
9 months ago
B) Excluding data features from the AI application to benefit a population
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Blondell
10 months ago
A) Ongoing auditing and monitoring of data that is used in AI applications
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Celeste
10 months ago
Excluding certain data features to benefit a population? Hmm, that sounds a bit too targeted for my liking. Fairness is all about the big picture.
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Mel
10 months ago
C) Using data that contains more examples of minority groups than majority groups
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Katie
10 months ago
A) Ongoing auditing and monitoring of data that is used in AI applications
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Louis
10 months ago
I believe option B) Excluding data features is not the best approach as it may lead to further bias.
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Leota
11 months ago
I agree with Janna. It's important to constantly check and review the data used in AI applications.
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Janna
11 months ago
I think the technique to mitigate bias is A) Ongoing auditing and monitoring of data.
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Renea
11 months ago
Ongoing auditing and monitoring of data is crucial to identify and address biases. Gotta keep those AI algorithms honest!
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Lynette
10 months ago
That's right, we need to make sure the data is diverse and representative.
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Clorinda
10 months ago
C) Using data that contains more examples of minority groups than majority groups
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German
10 months ago
A) Ongoing auditing and monitoring of data that is used in AI applications
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