Suggested Answer:
Reliability and safety: AI systems need to be reliable and safe in order to be trusted. It is important for a system to perform as it was originally designed and for it to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and champion/challenger methods should be integrated into the evaluation process. An AI system's performance can degrade over time, so a robust monitoring and model tracking process needs to be established to reactively and proactively measure the model's performance and retrain it, as necessary, to modernize it. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
I think “Reliability and safety” is correct option as per the following:
“To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It's also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing.
We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don't have unexpected performance failures, and don't evolve in ways that are inconsistent with original expectations”
I think everyone here is thinking of missing values should be "fixed" and unusual values be fixed as well. So you are coming from data transformation so data is accurate and the model will be a lot more accurate in predicting. That is not the point for this question.
This question is about not letting people who use the model predict personal information or private information which was used to train the model in the first place. So putting in missing values or odd values does skew the model prediction and yet at the same time it protect the privacy of the data used to train the model in the first place.
So answer is privacy and safety.
1. reliability and safety
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai#reliability-and-safety
AI systems need to be reliable and safe in order to be trusted. It's important for a system to perform as it was originally designed and for it to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and champion/challenger methods should be integrated into the evaluation process.
IMHO the answer given is correct. Unusual or missing values in the dataset can affect the validity of the training data because they do not allow the algorithm to understand based on which criteria it should perform the prediction in the specific case
There are 6 responsible AI principles.
'Reliability and Safety is one of them - which means - our AI system should be consistent with values and it should not create harm in the world.
Vote for C
A voting comment increases the vote count for the chosen answer by one.
Upvoting a comment with a selected answer will also increase the vote count towards that answer by one.
So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.
Pukk
Highly Voted 3 years, 12 months agosck
Highly Voted 3 years, 12 months agogills
Most Recent 2 weeks agoKanjati
3 years, 4 months agozellck
2 weeks, 2 days agordemontis
1 year, 3 months agobanglori
1 year, 6 months agoJKRowlings
2 years, 2 months agoEltooth
2 years, 3 months agoydu7312
2 years, 4 months agocormorant
2 years, 6 months agobeginnercloud
2 years, 10 months agosumanshu
3 years, 2 months agoLubellana
3 years, 3 months agoAVP_Riga
3 years, 3 months agoSQLNh
3 years, 4 months agodeveast
3 years, 5 months agoKanjati
3 years, 5 months ago