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CertiFair: A Framework for Certified Global Fairness of Neural Networks

CertiFair: A Framework for Certified Global Fairness of Neural Networks

20 May 2022
Haitham Khedr
Yasser Shoukry
    FedML
ArXivPDFHTML

Papers citing "CertiFair: A Framework for Certified Global Fairness of Neural Networks"

15 / 15 papers shown
Title
FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
Brian Hyeongseok Kim
Jingbo Wang
Chao Wang
26
1
0
05 Sep 2024
Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations
Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations
Vasisht Duddu
Oskari Jarvinen
Lachlan J. Gunn
Nirmal Asokan
64
1
0
25 Jun 2024
Relational DNN Verification With Cross Executional Bound Refinement
Relational DNN Verification With Cross Executional Bound Refinement
Debangshu Banerjee
Gagandeep Singh
AAML
23
5
0
16 May 2024
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
Tahsin Alamgir Kheya
Mohamed Reda Bouadjenek
Sunil Aryal
28
8
0
26 Mar 2024
FairProof : Confidential and Certifiable Fairness for Neural Networks
FairProof : Confidential and Certifiable Fairness for Neural Networks
Chhavi Yadav
A. Chowdhury
Dan Boneh
Kamalika Chaudhuri
MLAU
35
7
0
19 Feb 2024
Certification of Distributional Individual Fairness
Certification of Distributional Individual Fairness
Matthew Wicker
Vihari Piratla
Adrian Weller
19
1
0
20 Nov 2023
Privacy and Fairness in Federated Learning: on the Perspective of
  Trade-off
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Huiqiang Chen
Tianqing Zhu
Tao Zhang
Wanlei Zhou
Philip S. Yu
FedML
22
43
0
25 Jun 2023
Verifying Global Neural Network Specifications using Hyperproperties
Verifying Global Neural Network Specifications using Hyperproperties
David Boetius
Stefan Leue
AAML
18
0
0
21 Jun 2023
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using
  Bernstein Polynomial Activations and Precise Bound Propagation
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation
Haitham Khedr
Yasser Shoukry
39
4
0
22 May 2023
gRoMA: a Tool for Measuring the Global Robustness of Deep Neural
  Networks
gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks
Natan Levy
Raz Yerushalmi
Guy Katz
AAML
14
1
0
05 Jan 2023
Explainable Global Fairness Verification of Tree-Based Classifiers
Explainable Global Fairness Verification of Tree-Based Classifiers
Stefano Calzavara
Lorenzo Cazzaro
Claudio Lucchese
Federico Marcuzzi
19
2
0
27 Sep 2022
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms
  for Neural Networks
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks
Kiarash Mohammadi
Aishwarya Sivaraman
G. Farnadi
17
5
0
01 Jun 2022
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
296
4,203
0
23 Aug 2019
Output Reachable Set Estimation and Verification for Multi-Layer Neural
  Networks
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks
Weiming Xiang
Hoang-Dung Tran
Taylor T. Johnson
72
292
0
09 Aug 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
226
1,835
0
03 Feb 2017
1