Fairness Criteria, Exploring Fairness in Machine Learning

Fairness Criteria, Exploring Fairness in Machine Learning



MIT RES.EC-001 Exploring Fairness in Machine Learning, Spring 2020
Instructor: Mike Teodorescu
View the complete course: https://ocw.mit.edu/RES-EC-001S20
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This video presents the confusion matrix, including true negatives, true positives, false negatives, and false positives. It discusses how to choose between different fairness criteria such as demographic parity, equalized odds, and equalized opportunity.

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