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: https://www.youtube.com/playlist?list=PLUl4u3cNGP63IFQn8FklBOUhYVcmaxpOX 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.

Published 2020-10-01 · Watch on YouTube ↗
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