CertNexus AIP-210 Exam - Topic 1 Question 9 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 9
Topic #: 1

An AI practitioner incorporates risk considerations into a deployment plan and decides to log and store historical predictions for potential, future access requests.

Which ethical principle is this an example of?

AFairness

BPrivacy

CSafety

DTransparency

Show Suggested Answer

Suggested Answer: B

The final stage of the data management life cycle is data destruction, which is the process of securely deleting or erasing data that is no longer needed or relevant for the organization. Data destruction ensures that data is disposed of in compliance with any legal or regulatory requirements, as well as any internal policies or standards. Data destruction also protects the organization from potential data breaches, leaks, or thefts that could compromise its privacy and security. Data destruction can be performed using various methods, such as overwriting, degaussing, shredding, or incinerating

by Jovita at Dec 15, 2023, 01:58 AM

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Sylvia

8 months ago

Sounds a bit fishy to me, not sure about this one.

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Staci

9 months ago

Wait, are we sure this isn't just about Fairness?

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Celeste

9 months ago

Logging predictions helps with Safety too!

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Merilyn

9 months ago

I think it relates more to Privacy.

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Arletta

9 months ago

This is definitely about Transparency.

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Honey

9 months ago

I lean towards fairness because it’s about ensuring that the AI's decisions are accountable and fair over time.

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Adaline

10 months ago

This seems similar to a practice question we did on ethical principles, and I feel like safety could be a factor here too.

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Macy

10 months ago

I'm not entirely sure, but I remember something about privacy being important when it comes to storing historical data.

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Devon

10 months ago

I think this might relate to transparency since logging predictions could help users understand how decisions are made.

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Irving

10 months ago

I'm confident the answer is B. Privacy. Logging and storing historical predictions is a way to ensure transparency and accountability, which are important for protecting individual privacy.

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Carli

10 months ago

Okay, I've got it! Logging and storing historical predictions is an example of the ethical principle of privacy. The AI practitioner is preserving data for potential future access, which is important for maintaining privacy.

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Lenny

10 months ago

Hmm, I'm a bit unsure about this one. Logging and storing historical predictions could relate to a few different ethical principles. I'll need to think it through carefully.

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Ligia

10 months ago

This seems like a straightforward question about ethical principles in AI. I think the key is to focus on the specific details provided in the scenario.

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Graham

10 months ago

Ugh, I'm not sure I remember how to do this. Dividend growth calculations always trip me up. I better review my notes quickly.

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Carisa

10 months ago

Ah, I see what they're asking now. I think option B is the correct answer.

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Cory

10 months ago

Option A seems plausible, but I'll need to verify if there's actually a syntax error in the FORMAT statement.

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Denise

1 year ago

Ah, the old 'log everything and hope for the best' approach. Ethical, sure, but it's like trying to fact-check a politician's speech – good luck with that!

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Stephanie

1 year ago

Logging predictions, huh? I hope they have plenty of storage space, 'cause my code tends to make some pretty wild guesses sometimes.

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Lacresha

1 year ago

B) Privacy

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Chauncey

1 year ago

D) Transparency

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Iola

1 year ago

A) Fairness

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Felix

1 year ago

Hmm, I'd say fairness. Keeping track of the predictions helps ensure the AI system is being fair and unbiased in its decision-making. Transparency is cool too, I guess.

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Stanton

1 year ago

Safety, for sure. Logging predictions helps identify potential risks or issues down the line. Proactive approach, I like it.

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Elouise

1 year ago

C) Safety

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Gail

1 year ago

C: D) Transparency

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Lashon

1 year ago

B: Proactive approach, I like it.

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Cordelia

1 year ago

B) Privacy

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Renea

1 year ago

A) Fairness

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Albert

1 year ago

A: Safety, for sure. Logging predictions helps identify potential risks or issues down the line.

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Casey

1 year ago

I'm not sure, but I think it could also be A) Fairness. By storing historical predictions, the AI practitioner is ensuring fairness in future access requests.

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Gregg

1 year ago

I think this is about privacy. Storing people's prediction data could be a sensitive issue if not handled properly. Gotta be careful with that.

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Leota

1 year ago

B: I agree, it's important to be transparent about how the data is being used and stored.

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Bo

1 year ago

A: I think it's more about transparency. Keeping a record of predictions can help ensure accountability.

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Malika

1 year ago

Transparency is also important in this case, so people know how their data is being used and stored.

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Alaine

1 year ago

I agree, privacy is definitely a key concern when it comes to storing sensitive data like predictions.

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Dyan

1 year ago

I agree with Fidelia. It's important to be transparent about how AI predictions are used.

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Fidelia

1 year ago

I think it's D) Transparency.

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Fletcher

1 year ago

Definitely transparency. Logging and storing historical predictions allows for future access and accountability. That's key for ethical AI principles.

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