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Listening Quiz: Thảo Luận Về Các Ràng Buộc Công Bằng Trong Thiết Kế Mô Hình – Easy Level
Th8 18, 2024
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bởi
Anna Phạm
trong
Tiếng Anh dành cho Nhà khoa học dữ liệu (English for Data Scientist)
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1.
What are Olivia and Michael mainly discussing?
Why the model should only focus on accuracy
How to make the model faster during training
How to remove errors from the training data
How to add fairness constraints to improve the model
Explanation:
They discuss adding fairness constraints to the model so it can be accurate and fair at the same time.
2.
What does Olivia suggest using to improve fairness?
A different objective function for each group
Constraints that balance errors across groups
A test that removes all bias automatically
A larger training dataset
Explanation:
Olivia says they can use constraints that balance errors across groups.
3.
What does Olivia say about the effect on training time?
It will not change at all
It will stop training from working
It will become much faster
It might take a bit longer
Explanation:
Olivia says it might take a bit longer, but it is important.
4.
What can be inferred about Michael’s view after the discussion?
He disagrees with adding fairness constraints
He wants to delay the project indefinitely
He believes the model should ignore fairness
He thinks fairness constraints could be useful
Explanation:
Michael responds positively, says the idea sounds good, and agrees to add the constraints and test the model soon.
5.
Why does Olivia want to add fairness constraints?
To reduce the number of groups in the data
To help the model be fair while remaining accurate
To make the model easier to understand
To eliminate the need for testing
Explanation:
Olivia says the model will try to be accurate and fair at the same time, and that this will make the model fair for everyone.
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