On Adversarial Bias and the Robustness of Fair Machine Learning

15 June 2020

Papers citing "On Adversarial Bias and the Robustness of Fair Machine Learning"

7 / 7 papers shown

Title
Adversarial Attacks on Fairness of Graph Neural Networks Binchi Zhang Yushun Dong Chen Chen Yada Zhu Minnan Luo Jundong Li 38 3 0 20 Oct 2023
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition Luke E. Richards Edward Raff Cynthia Matuszek AAML 16 2 0 17 Feb 2023
Fairness-aware Regression Robust to Adversarial Attacks Yulu Jin Lifeng Lai FaML OOD 18 4 0 04 Nov 2022
Sample Selection for Fair and Robust Training Yuji Roh Kangwook Lee Steven Euijong Whang Changho Suh 13 61 0 27 Oct 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 22 293 0 18 Oct 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,203 0 23 Aug 2019
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 233 673 0 17 Feb 2018