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FADE - FAir Double Ensemble Learning for Observable and Counterfactual Outcomes

Offered By: Association for Computing Machinery (ACM) via YouTube

Tags

ACM FAccT Conference Courses Machine Learning Courses Ensemble Learning Courses Predictive Modeling Courses

Course Description

Overview

Explore a 15-minute conference talk presented at an Association for Computing Machinery (ACM) event that delves into FADE, a novel approach to fair double ensemble learning for observable and counterfactual outcomes. Learn about the innovative techniques developed by researchers Alan Mishler and Edward H. Kennedy to address fairness concerns in machine learning models, particularly in scenarios involving both observable and counterfactual outcomes. Gain insights into how FADE can potentially improve decision-making processes in various fields where fairness and equity are crucial considerations.

Syllabus

FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes


Taught by

ACM FAccT Conference

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