On Adversarial Bias and the Robustness of Fair Machine Learning

On Adversarial Bias and the Robustness of Fair Machine Learning

15 June 2020

S. K. Murakonda

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

11 / 11 papers shown

Title
Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers Huan Tian Guangsheng Zhang Bo Liu Tianqing Zhu Ming Ding Wanlei Zhou 53 0 0 08 Mar 2025
Adversarial Attacks on Fairness of Graph Neural Networks Binchi Zhang Yushun Dong Chen Chen Yada Zhu Minnan Luo Jundong Li 41 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 29 4 0 04 Nov 2022
Subverting Fair Image Search with Generative Adversarial Perturbations A. Ghosh Matthew Jagielski Chris L. Wilson 22 7 0 05 May 2022
Sample Selection for Fair and Robust Training Yuji Roh Kangwook Lee Steven Euijong Whang Changho Suh 21 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 36 293 0 18 Oct 2021
Technical Challenges for Training Fair Neural Networks Valeriia Cherepanova V. Nanda Micah Goldblum John P. Dickerson Tom Goldstein FaML 17 22 0 12 Feb 2021
Subpopulation Data Poisoning Attacks Matthew Jagielski Giorgio Severi Niklas Pousette Harger Alina Oprea AAML SILM 21 112 0 24 Jun 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,212 0 23 Aug 2019
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 233 674 0 17 Feb 2018