Fairness Without Imputation: A Decision Tree Approach for Fair Prediction With Missing Values
Offered By: Simons Institute via YouTube
Course Description
Overview
Explore a 35-minute conference talk by Haewon Jeong from UC Santa Barbara, presented at the Simons Institute, addressing the challenge of fair prediction with missing values in machine learning. Delve into a novel decision tree approach that achieves fairness without relying on imputation techniques. Learn how this method contributes to the field of information-theoretic methods for trustworthy machine learning, offering insights into maintaining fairness in predictive models when dealing with incomplete data sets.
Syllabus
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
Taught by
Simons Institute
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