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Framework for Evaluating Faithfulness of Local Explanations

Framework for Evaluating Faithfulness of Local Explanations

1 February 2022
S. Dasgupta
Nave Frost
Michal Moshkovitz
    FAtt
ArXivPDFHTML

Papers citing "Framework for Evaluating Faithfulness of Local Explanations"

11 / 11 papers shown
Title
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
Sonal Allana
Mohan Kankanhalli
Rozita Dara
32
0
0
05 May 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
Chhavi Yadav
Evan Monroe Laufer
Dan Boneh
Kamalika Chaudhuri
91
0
0
06 Feb 2025
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics
Lukas Klein
Carsten T. Lüth
U. Schlegel
Till J. Bungert
Mennatallah El-Assady
Paul F. Jäger
XAI
ELM
42
2
0
03 Jan 2025
A Tale of Two Imperatives: Privacy and Explainability
A Tale of Two Imperatives: Privacy and Explainability
Supriya Manna
Niladri Sett
124
0
0
30 Dec 2024
GIFT: A Framework for Global Interpretable Faithful Textual Explanations of Vision Classifiers
GIFT: A Framework for Global Interpretable Faithful Textual Explanations of Vision Classifiers
Éloi Zablocki
Valentin Gerard
Amaia Cardiel
Eric Gaussier
Matthieu Cord
Eduardo Valle
81
0
0
23 Nov 2024
A Fresh Look at Sanity Checks for Saliency Maps
A Fresh Look at Sanity Checks for Saliency Maps
Anna Hedström
Leander Weber
Sebastian Lapuschkin
Marina M.-C. Höhne
FAtt
LRM
48
5
0
03 May 2024
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable
  Estimators with MetaQuantus
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Anna Hedström
P. Bommer
Kristoffer K. Wickstrom
Wojciech Samek
Sebastian Lapuschkin
Marina M.-C. Höhne
37
21
0
14 Feb 2023
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural
  Network Explanations and Beyond
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Anna Hedström
Leander Weber
Dilyara Bareeva
Daniel G. Krakowczyk
Franz Motzkus
Wojciech Samek
Sebastian Lapuschkin
Marina M.-C. Höhne
XAI
ELM
21
168
0
14 Feb 2022
Revisiting The Evaluation of Class Activation Mapping for
  Explainability: A Novel Metric and Experimental Analysis
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis
Samuele Poppi
Marcella Cornia
Lorenzo Baraldi
Rita Cucchiara
FAtt
131
33
0
20 Apr 2021
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
Model Interpretability through the Lens of Computational Complexity
Model Interpretability through the Lens of Computational Complexity
Pablo Barceló
Mikaël Monet
Jorge A. Pérez
Bernardo Subercaseaux
126
94
0
23 Oct 2020
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