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FNNC: Achieving Fairness through Neural Networks
v1v2v3 (latest)

FNNC: Achieving Fairness through Neural Networks

1 November 2018
P. Manisha
Sujit Gujar
ArXiv (abs)PDFHTML

Papers citing "FNNC: Achieving Fairness through Neural Networks"

22 / 22 papers shown
Title
Machine Learning Fairness for Depression Detection using EEG Data
Machine Learning Fairness for Depression Detection using EEG Data
Angus Man Ho Kwok
Jiaee Cheong
Sinan Kalkan
Hatice Gunes
106
2
0
30 Jan 2025
fairret: a Framework for Differentiable Fairness Regularization Terms
fairret: a Framework for Differentiable Fairness Regularization Terms
Maarten Buyl
Marybeth Defrance
T. D. Bie
FedML
71
4
0
26 Oct 2023
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in
  Medical Image Analysis
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
Raman Dutt
Ondrej Bohdal
Sotirios A. Tsaftaris
Timothy M. Hospedales
122
14
0
08 Oct 2023
Linking convolutional kernel size to generalization bias in face
  analysis CNNs
Linking convolutional kernel size to generalization bias in face analysis CNNs
Hao Liang
J. O. Caro
Vikram Maheshri
Ankit B. Patel
Guha Balakrishnan
CVBMCML
68
0
0
07 Feb 2023
Within-group fairness: A guidance for more sound between-group fairness
Within-group fairness: A guidance for more sound between-group fairness
Sara Kim
Kyusang Yu
Yongdai Kim
FaML
124
1
0
20 Jan 2023
Robustness Disparities in Face Detection
Robustness Disparities in Face Detection
Samuel Dooley
George Z. Wei
Tom Goldstein
John P. Dickerson
CVBM
90
9
0
29 Nov 2022
Discrimination and Class Imbalance Aware Online Naive Bayes
Discrimination and Class Imbalance Aware Online Naive Bayes
Maryam Badar
M. Fisichella
Vasileios Iosifidis
Wolfgang Nejdl
FaML
30
1
0
09 Nov 2022
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face
  Recognition
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Samuel Dooley
R. Sukthanker
John P. Dickerson
Colin White
Frank Hutter
Micah Goldblum
CVBM
132
23
0
18 Oct 2022
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort
Zhenpeng Chen
Jie M. Zhang
Mark Harman
Federica Sarro
FaMLAI4CE
107
177
0
14 Jul 2022
Learning to Teach Fairness-aware Deep Multi-task Learning
Learning to Teach Fairness-aware Deep Multi-task Learning
Arjun Roy
Eirini Ntoutsi
76
7
0
16 Jun 2022
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair
  Neural Networks
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Michael Lohaus
Matthäus Kleindessner
K. Kenthapadi
Francesco Locatello
Chris Russell
91
12
0
09 Apr 2022
Optimal Transport of Classifiers to Fairness
Optimal Transport of Classifiers to Fairness
Maarten Buyl
T. D. Bie
FaML
44
11
0
08 Feb 2022
Are Commercial Face Detection Models as Biased as Academic Models?
Samuel Dooley
George Z. Wei
Tom Goldstein
John P. Dickerson
CVBM
80
4
0
25 Jan 2022
Comparing Human and Machine Bias in Face Recognition
Comparing Human and Machine Bias in Face Recognition
Samuel Dooley
Ryan Downing
George Z. Wei
N. Shankar
Bradon Thymes
...
Olufemi Obiwumi
Valeriia Cherepanova
Micah Goldblum
John P. Dickerson
Tom Goldstein
CVBM
191
12
0
15 Oct 2021
Gradual (In)Compatibility of Fairness Criteria
Gradual (In)Compatibility of Fairness Criteria
Corinna Hertweck
T. Raz
79
12
0
09 Sep 2021
Federated Learning Meets Fairness and Differential Privacy
Federated Learning Meets Fairness and Differential Privacy
P. Manisha
Sankarshan Damle
Sujit Gujar
FedML
81
21
0
23 Aug 2021
Representative & Fair Synthetic Data
Representative & Fair Synthetic Data
P. Tiwald
Alexandra Ebert
Daniel Soukup
63
12
0
07 Apr 2021
Technical Challenges for Training Fair Neural Networks
Technical Challenges for Training Fair Neural Networks
Valeriia Cherepanova
V. Nanda
Micah Goldblum
John P. Dickerson
Tom Goldstein
FaML
71
22
0
12 Feb 2021
Metrics and methods for a systematic comparison of fairness-aware
  machine learning algorithms
Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms
Gareth Jones
James M. Hickey
Pietro G. Di Stefano
C. Dhanjal
Laura C. Stoddart
V. Vasileiou
FaML
60
21
0
08 Oct 2020
Fairness by Explicability and Adversarial SHAP Learning
Fairness by Explicability and Adversarial SHAP Learning
James M. Hickey
Pietro G. Di Stefano
V. Vasileiou
FAttFedML
123
19
0
11 Mar 2020
Counterfactual fairness: removing direct effects through regularization
Counterfactual fairness: removing direct effects through regularization
Pietro G. Di Stefano
James M. Hickey
V. Vasileiou
FaML
128
19
0
25 Feb 2020
Perfectly Parallel Fairness Certification of Neural Networks
Perfectly Parallel Fairness Certification of Neural Networks
Caterina Urban
M. Christakis
Valentin Wüstholz
Fuyuan Zhang
105
72
0
05 Dec 2019
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