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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?
How to add fairness constraints to improve the model
How to make the model faster during training
How to remove errors from the training data
Why the model should only focus on accuracy
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 larger training dataset
Constraints that balance errors across groups
A different objective function for each group
A test that removes all bias automatically
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 become much faster
It will stop training from working
It will not change at all
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 thinks fairness constraints could be useful
He disagrees with adding fairness constraints
He believes the model should ignore fairness
He wants to delay the project indefinitely
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 make the model easier to understand
To help the model be fair while remaining accurate
To eliminate the need for testing
To reduce the number of groups in the data
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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