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Marrying Fairness and Explainability in Supervised Learning
ACM FAccT Conference
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Marrying Fairness and Explainability in Supervised Learning
ACM FAccT Conference
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Stanford Seminar - ML Explainability Part 1 I Overview and Motivation for Explainability
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Fairness Criteria, Exploring Fairness in Machine Learning
MIT OpenCourseWare
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Fairness, Accountability and Transparency in Machine Learning: Part One
NYU School of Law
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Robust Causal Inference using Double/Debiased Machine Learning: A Guide for Empirical Research
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Lecture 1 "Supervised Learning Setup" -Cornell CS4780 Machine Learning for Decision Making SP17
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53:46
Tutorial: Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU
ACM FAccT Conference
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1:09:58