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1901.09749
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Fairwashing: the risk of rationalization
28 January 2019
Ulrich Aïvodji
Hiromi Arai
O. Fortineau
Sébastien Gambs
Satoshi Hara
Alain Tapp
FaML
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Papers citing
"Fairwashing: the risk of rationalization"
50 / 87 papers shown
Interpretable Model-Aware Counterfactual Explanations for Random Forest
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286
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12 Oct 2025
Secure human oversight of AI: Threat modeling in a socio-technical context
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324
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15 Sep 2025
Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act
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Bill Marino
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194
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02 Jun 2025
Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects
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472
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24 May 2025
Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness
Héber H. Arcolezi
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253
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0
12 May 2025
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Rushabh Solanki
Meghana Bhange
Ulrich Aïvodji
Elliot Creager
314
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0
09 May 2025
Beware of "Explanations" of AI
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Galit Shmueli
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Christian Janiesch
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Claudia Perlich
Wouter Verbeke
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432
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09 Apr 2025
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334
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ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
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Evan Monroe Laufer
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Kamalika Chaudhuri
577
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06 Feb 2025
Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse
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Berk Ustun
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685
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29 Oct 2024
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Thibault Laugel
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315
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22 May 2024
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Astrid Schomacker
Timo Speith
Jakob Schöffer
Lena Kästner
Niklas Kühl
605
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29 Apr 2024
SIDEs: Separating Idealization from Deceptive Explanations in xAI
Emily Sullivan
343
6
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25 Apr 2024
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
Jon Vadillo
Roberto Santana
J. A. Lozano
Marta Z. Kwiatkowska
AAML
BDL
622
1
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20 Mar 2024
Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias
Conference on Fairness, Accountability and Transparency (FAccT), 2024
Sierra Wyllie
Ilia Shumailov
Nicolas Papernot
290
61
0
12 Mar 2024
Black-Box Access is Insufficient for Rigorous AI Audits
Conference on Fairness, Accountability and Transparency (FAccT), 2024
Stephen Casper
Carson Ezell
Charlotte Siegmann
Noam Kolt
Taylor Lynn Curtis
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Michael Gerovitch
David Bau
Max Tegmark
David M. Krueger
Dylan Hadfield-Menell
AAML
722
153
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25 Jan 2024
X Hacking: The Threat of Misguided AutoML
Rahul Sharma
Sergey Redyuk
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Andrea Sipka
Eyke Hüllermeier
Sebastian Vollmer
David Selby
557
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16 Jan 2024
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
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Ulrich Aïvodji
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Marie-José Huguet
Mohamed Siala
FaML
364
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22 Dec 2023
Survey on AI Ethics: A Socio-technical Perspective
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Dave Mbiazi
Meghana Bhange
Maryam Babaei
Ivaxi Sheth
Patrik Kenfack
Samira Ebrahimi Kahou
472
11
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28 Nov 2023
Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms
Alessandro Castelnovo
Nicole Inverardi
Gabriele Nanino
Ilaria Giuseppina Penco
D. Regoli
FaML
326
4
0
21 Nov 2023
Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks
Aditi Mishra
Sajjadur Rahman
H. Kim
Kushan Mitra
Estevam R. Hruschka
428
12
0
09 Nov 2023
A Critical Survey on Fairness Benefits of Explainable AI
Luca Deck
Jakob Schoeffer
Maria De-Arteaga
Niklas Kühl
663
47
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15 Oct 2023
One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions
Conference on Fairness, Accountability and Transparency (FAccT), 2023
Jan Simson
Florian Pfisterer
Christoph Kern
396
17
0
31 Aug 2023
Probabilistic Dataset Reconstruction from Interpretable Models
Julien Ferry
Ulrich Aïvodji
Sébastien Gambs
Marie-José Huguet
Mohamed Siala
254
9
0
29 Aug 2023
Fairness Explainability using Optimal Transport with Applications in Image Classification
Philipp Ratz
Franccois Hu
Arthur Charpentier
242
1
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22 Aug 2023
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem
S. Goethals
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365
13
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24 Jun 2023
Don't trust your eyes: on the (un)reliability of feature visualizations
International Conference on Machine Learning (ICML), 2023
Robert Geirhos
Roland S. Zimmermann
Blair Bilodeau
Wieland Brendel
Been Kim
FAtt
OOD
561
38
0
07 Jun 2023
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Information Fusion (Inf. Fusion), 2023
Hubert Baniecki
P. Biecek
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635
141
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06 Jun 2023
Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing
Jade Garcia Bourrée
Erwan Le Merrer
Gilles Tredan
Benoit Rottembourg
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170
1
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Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
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Natalia Díaz Rodríguez
Javier Del Ser
Mark Coeckelbergh
Marcos López de Prado
E. Herrera-Viedma
Francisco Herrera
XAI
390
528
0
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Disagreement amongst counterfactual explanations: How transparency can be deceptive
Dieter Brughmans
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231
7
0
25 Apr 2023
Learning Optimal Fair Scoring Systems for Multi-Class Classification
IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2022
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Julien Ferry
Marie-José Huguet
FaML
365
12
0
11 Apr 2023
Why is plausibility surprisingly problematic as an XAI criterion?
Weina Jin
Xiaoxiao Li
Ghassan Hamarneh
502
10
0
30 Mar 2023
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Julien Ferry
Gabriel Laberge
Ulrich Aïvodji
325
8
0
08 Mar 2023
Five policy uses of algorithmic transparency and explainability
Matthew R. O’Shaughnessy
478
1
0
06 Feb 2023
Explainable AI does not provide the explanations end-users are asking for
Savio Rozario
G. Cevora
XAI
232
3
0
25 Jan 2023
Tensions Between the Proxies of Human Values in AI
Teresa Datta
D. Nissani
Max Cembalest
Akash Khanna
Haley Massa
John P. Dickerson
244
4
0
14 Dec 2022
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making
International Conference on Human Factors in Computing Systems (CHI), 2022
Jakob Schoeffer
Maria De-Arteaga
Niklas Kuehl
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553
84
0
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SoK: Explainable Machine Learning for Computer Security Applications
European Symposium on Security and Privacy (Euro S&P), 2022
A. Nadeem
D. Vos
Clinton Cao
Luca Pajola
Simon Dieck
Robert Baumgartner
S. Verwer
418
67
0
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OpenXAI: Towards a Transparent Evaluation of Model Explanations
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Dan Ley
Satyapriya Krishna
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Isha Puri
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619
190
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357
42
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Fool SHAP with Stealthily Biased Sampling
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Ulrich Aïvodji
Satoshi Hara
M. Marchand
Foutse Khomh
MLAU
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254
5
0
30 May 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
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Jessica Dai
Sohini Upadhyay
Ulrich Aïvodji
Stephen H. Bach
Himabindu Lakkaraju
326
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The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
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Haoran Zhang
Kimia Hamidieh
Thomas Hartvigsen
Frank Rudzicz
Marzyeh Ghassemi
335
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A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
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Manish Nagireddy
Logan Stapleton
H. Cheng
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Steven Wu
Hoda Heidari
304
17
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Backdooring Explainable Machine Learning
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Lukas Peter
Christian Wressnegger
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231
5
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20 Apr 2022
Robustness and Usefulness in AI Explanation Methods
Erick Galinkin
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289
2
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07 Mar 2022
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144
3
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Algorithmic audits of algorithms, and the law
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Ronan Pons
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222
15
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15 Feb 2022
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