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Fairness via Explanation Quality: Evaluating Disparities in the Quality
  of Post hoc Explanations

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

15 May 2022
Jessica Dai
Sohini Upadhyay
Ulrich Aïvodji
Stephen H. Bach
Himabindu Lakkaraju
ArXivPDFHTML

Papers citing "Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations"

11 / 11 papers shown
Title
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Mahdi Dhaini
Ege Erdogan
Nils Feldhus
Gjergji Kasneci
49
0
0
02 May 2025
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
Evandro S. Ortigossa
Fábio F. Dias
Brian Barr
Claudio T. Silva
L. G. Nonato
FAtt
61
2
0
25 Apr 2024
Accurate estimation of feature importance faithfulness for tree models
Accurate estimation of feature importance faithfulness for tree models
Mateusz Gajewski
Adam Karczmarz
Mateusz Rapicki
Piotr Sankowski
37
0
0
04 Apr 2024
Procedural Fairness in Machine Learning
Procedural Fairness in Machine Learning
Ziming Wang
Changwu Huang
Xin Yao
FaML
50
0
0
02 Apr 2024
On Explaining Unfairness: An Overview
On Explaining Unfairness: An Overview
Christos Fragkathoulas
Vasiliki Papanikou
Danae Pla Karidi
E. Pitoura
XAI
FaML
19
2
0
16 Feb 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu
S. Szyller
Nadarajah Asokan
AAML
47
2
0
07 Dec 2023
The future of human-centric eXplainable Artificial Intelligence (XAI) is
  not post-hoc explanations
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
Vinitra Swamy
Jibril Frej
Tanja Kaser
34
14
0
01 Jul 2023
Tensions Between the Proxies of Human Values in AI
Tensions Between the Proxies of Human Values in AI
Teresa Datta
D. Nissani
Max Cembalest
Akash Khanna
Haley Massa
John P. Dickerson
34
2
0
14 Dec 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of
  Explanations
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan
Haoran Zhang
Kimia Hamidieh
Thomas Hartvigsen
Frank Rudzicz
Marzyeh Ghassemi
38
78
0
06 May 2022
How can I choose an explainer? An Application-grounded Evaluation of
  Post-hoc Explanations
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
Sérgio Jesus
Catarina Belém
Vladimir Balayan
João Bento
Pedro Saleiro
P. Bizarro
João Gama
136
120
0
21 Jan 2021
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
257
3,690
0
28 Feb 2017
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