Removing biased data to improve fairness and accuracy

5 February 2021

Papers citing "Removing biased data to improve fairness and accuracy"

8 / 8 papers shown

Title
FAIR-FATE: Fair Federated Learning with Momentum Teresa Salazar Miguel X. Fernandes Helder Araújo Pedro Abreu FedML 30 18 0 27 Sep 2022
Enforcing Delayed-Impact Fairness Guarantees Aline Weber Blossom Metevier Yuriy Brun Philip S. Thomas Bruno Castro da Silva FaML 22 9 0 24 Aug 2022
Accurate Fairness: Improving Individual Fairness without Trading Accuracy Xuran Li Peng Wu Jing Su FaML 31 17 0 18 May 2022
Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition Cailin Winston Caleb Winston Chloe N. Winston Claris Winston Cleah Winston Rajesh P. N. Rao René Just 11 1 0 20 Mar 2022
ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic Yoad Tewel Yoav Shalev Idan Schwartz Lior Wolf VLM 34 192 0 29 Nov 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 320 4,203 0 23 Aug 2019
A statistical framework for fair predictive algorithms K. Lum J. Johndrow FaML 172 104 0 25 Oct 2016
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016