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2201.10295
Cited By
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts
25 January 2022
Sebastian Bordt
Michèle Finck
Eric Raidl
U. V. Luxburg
AILaw
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Papers citing
"Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts"
13 / 13 papers shown
Title
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning
Volkan Bakir
Polat Goktas
Sureyya Akyuz
43
0
0
26 Apr 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
Chhavi Yadav
Evan Monroe Laufer
Dan Boneh
Kamalika Chaudhuri
83
0
0
06 Feb 2025
Explainable AI needs formal notions of explanation correctness
Stefan Haufe
Rick Wilming
Benedict Clark
Rustam Zhumagambetov
Danny Panknin
Ahcène Boubekki
XAI
26
0
0
22 Sep 2024
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Xi Xin
Giles Hooker
Fei Huang
AAML
38
6
0
29 Apr 2024
The Case Against Explainability
Hofit Wasserman Rozen
N. Elkin-Koren
Ran Gilad-Bachrach
AILaw
ELM
6
0
0
20 May 2023
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK
L. Nannini
Agathe Balayn
A. Smith
6
37
0
20 Apr 2023
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
B. Keenan
Kacper Sokol
6
7
0
07 Feb 2023
COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data
Jonas C. Ditz
Bernhard Reuter
Nícolas Pfeifer
19
1
0
02 Dec 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust
H. Fokkema
R. D. Heide
T. Erven
FAtt
42
18
0
31 May 2022
Unfooling Perturbation-Based Post Hoc Explainers
Zachariah Carmichael
Walter J. Scheirer
AAML
48
14
0
29 May 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna
Tessa Han
Alex Gu
Steven Wu
S. Jabbari
Himabindu Lakkaraju
172
183
0
03 Feb 2022
Convolutional Motif Kernel Networks
Jonas C. Ditz
Bernhard Reuter
N. Pfeifer
FAtt
8
2
0
03 Nov 2021
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
Markus Langer
Daniel Oster
Timo Speith
Holger Hermanns
Lena Kästner
Eva Schmidt
Andreas Sesing
Kevin Baum
XAI
43
415
0
15 Feb 2021
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