Exam AI-900 topic 1 question 20 discussion

Actual exam question from Microsoft's AI-900

Question #: 20
Topic #: 1

DRAG DROP -
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:

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Suggested Answer:

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

by Phemmy86 at Oct. 26, 2021, 1:54 p.m.

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Phemmy86

Highly Voted 3 years, 6 months ago

Ans is correct

upvoted 16 times

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idioteque

Highly Voted 2 years, 9 months ago

For me to remember xD. Fairness = discriminate Privacy & Security = personal data Transparency = decision-making

upvoted 13 times

idioteque

2 years, 9 months ago

Transparency = should be "recorded"

upvoted 9 times

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saema

Most Recent 4 months, 1 week ago

zellck Most Recent 3 months, 3 weeks ago 1. Fairness 2. Privacy and security 3. Transparency

upvoted 1 times

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zellck

8 months ago

1. Fairness 2. Privacy and security 3. Transparency https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai#fairness Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.

upvoted 3 times

zellck

1 year, 11 months ago

https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai#privacy-and-security A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy. Azure differential privacy protects and preserves privacy by randomizing data and adding noise to conceal personal information from data scientists.

upvoted 2 times

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zellck

1 year, 11 months ago

https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai#transparency Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Snapshots within Azure Machine Learning workspaces support transparency by recording or retraining all training-related assets and metrics involved in the experiment.

upvoted 2 times

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sai7777

1 year ago

What is the difference between "fairness" and "inclusion"?

upvoted 2 times

sujitwarrier11

12 months ago

Well inclusiveness is about keeping all types of users in mind while creating the AI application. Eg Face recognition should work for all skin tones. Fairness is about getting the same output regardless of the type of person. For eg, An AI loan processing application should not accept or deny application based on gender or race

upvoted 5 times

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