To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods

7 February 2023

Liming Zhu

Papers citing "To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods"

3 / 3 papers shown

Title
Machine Unlearning: Linear Filtration for Logit-based Classifiers Thomas Baumhauer Pascal Schöttle Matthias Zeppelzauer MU 102 130 0 07 Feb 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,187 0 23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,082 0 24 Oct 2016