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Salesforce AI Associate Exam - Topic 2 Question 47 Discussion

What is one technique to mitigate bias and ensure fairness in AI applications?
A) Ongoing auditing and monitoring of data that is used in AI applications
B) Excluding data features from the Al application to benefit a population
C) Using data that contains more examples of minority groups than majority groups

Salesforce AI Associate Exam - Topic 2 Question 47 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam
Question #: 47
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 Jose at Aug 15, 2025, 02:15 PM

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Carlee
8 months ago
Totally agree, monitoring keeps things in check!
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Kerrie
9 months ago
Really? Can we trust that method to actually work?
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Maia
9 months ago
I think excluding data features can lead to more bias.
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Jina
9 months ago
More examples of minority groups is a solid approach!
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Teri
10 months ago
Ongoing auditing is crucial for fair AI!
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Royce
10 months ago
I definitely remember discussing the importance of auditing data, so I’m leaning towards that option for this question.
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Felix
10 months ago
Using more examples of minority groups sounds familiar, but I can't recall if that's a technique or just a suggestion from a lecture.
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Yuriko
10 months ago
I remember a practice question about excluding certain features to reduce bias, but I feel like that might not always lead to fairness.
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Isaac
10 months ago
I think ongoing auditing and monitoring of data is really important, but I'm not entirely sure if that's the best answer here.
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Tegan
11 months ago
Wait, I'm confused. Aren't we supposed to be avoiding bias, not introducing it through the data? I'm not sure any of these options are really the best approach. Guess I'll have to do some more research before answering this one.
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Viva
11 months ago
Okay, I've got an idea. Using data that contains more examples of minority groups could help ensure fairness, so I think I'll go with that option. Seems like the most direct way to address bias.
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Annalee
11 months ago
Hmm, I'm a bit unsure about this one. Excluding data features to benefit a population seems like it could introduce other biases, so I'm not sure that's the best approach. I'll have to think this through carefully.
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Raylene
11 months ago
This seems like a straightforward question about mitigating bias in AI. I think the key is ongoing auditing and monitoring of the data used, so that's the answer I'll go with.
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Pearlene
11 months ago
Hah, excluding data features? That's like trying to hide your vegetables in the mashed potatoes, it's just gonna make the problem worse. Gotta face that bias head-on, my dude.
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Lucy
12 months ago
Option A all the way! Monitoring the data is key to making sure the AI doesn't pick up on any sneaky biases. Can't have our AI overlords discriminating against the little guy, right?
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Stacey
12 months ago
Ongoing auditing and monitoring of data? Sounds like a lot of work, but I guess it's better than excluding data features or faking diversity. Gotta keep those AI models honest, you know?
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Krissy
8 months ago
Yes! Honest models lead to fairer outcomes.
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Vernice
8 months ago
Totally agree! Can't just ignore the data features.
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Justine
9 months ago
Ongoing auditing is crucial! It keeps AI in check.
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Ty
9 months ago
Faking diversity is a slippery slope. Real examples matter!
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Soledad
12 months ago
I agree with Karina. It's important to regularly check the data for bias.
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Karina
1 year ago
I think the answer is A) Ongoing auditing and monitoring of data.
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