FADE - FAir Double Ensemble Learning for Observable and Counterfactual Outcomes

FADE - FAir Double Ensemble Learning for Observable and Counterfactual Outcomes

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.