Fairness Criteria Exploring Fairness in Machine Learning

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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 • YouTube Playlist:    • MIT RES.EC-001 Exploring Fairness in ...   • 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. • License: Creative Commons BY-NC-SA • More information at https://ocw.mit.edu/terms • More courses at https://ocw.mit.edu • Support OCW at http://ow.ly/a1If50zVRlQ • We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

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