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1806.08049
Cited By
On the Robustness of Interpretability Methods
21 June 2018
David Alvarez-Melis
Tommi Jaakkola
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Papers citing
"On the Robustness of Interpretability Methods"
50 / 302 papers shown
SX-GeoTree: Self-eXplaining Geospatial Regression Tree Incorporating the Spatial Similarity of Feature Attributions
Chaogui Kang
Lijian Luo
Qingfeng Guan
Yu Liu
136
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0
25 Nov 2025
Correlation-Aware Feature Attribution Based Explainable AI
Poushali Sengupta
Yan Zhang
Frank Eliassen
Sabita Maharjan
123
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0
20 Nov 2025
CID: Measuring Feature Importance Through Counterfactual Distributions
Eddie Conti
Álvaro Parafita
Axel Brando
FAtt
CML
512
0
0
19 Nov 2025
Fair and Explainable Credit-Scoring under Concept Drift: Adaptive Explanation Frameworks for Evolving Populations
Shivogo John
FAtt
572
9
0
05 Nov 2025
Before the Clinic: Transparent and Operable Design Principles for Healthcare AI
Alexander Bakumenko
Aaron J. Masino
Janine Hoelscher
195
1
0
31 Oct 2025
Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF)
Robert Gigiu
148
0
0
24 Oct 2025
ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
Utsav Nareti
Suraj Kumar
Soumya Pandey
S. Chattopadhyay
Chandranath Adak
VLM
207
0
0
14 Oct 2025
Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness
Ilaria Vascotto
Alex Rodriguez
Alessandro Bonaita
Luca Bortolussi
63
0
0
13 Oct 2025
o-MEGA: Optimized Methods for Explanation Generation and Analysis
Ľuboš Kriš
Jaroslav Kopčan
Qiwei Peng
Andrej Ridzik
Marcel Veselý
Martin Tamajka
224
0
0
30 Sep 2025
On The Variability of Concept Activation Vectors
Julia Wenkmann
Damien Garreau
AAML
153
1
0
28 Sep 2025
Evaluating the stability of model explanations in instance-dependent cost-sensitive credit scoring
European Journal of Operational Research (EJOR), 2025
Matteo Ballegeer
Matthias Bogaert
Dries F. Benoit
FAtt
215
6
0
01 Sep 2025
How can we trust opaque systems? Criteria for robust explanations in XAI
Florian J. Boge
Annika Schuster
AAML
164
0
0
18 Aug 2025
On Spectral Properties of Gradient-based Explanation Methods
European Conference on Computer Vision (ECCV), 2025
Amir Mehrpanah
Erik Englesson
Hossein Azizpour
FAtt
191
1
0
14 Aug 2025
Beyond Technocratic XAI: The Who, What & How in Explanation Design
Ruchira Dhar
Stephanie Brandl
Ninell Oldenburg
Anders Søgaard
213
0
0
12 Aug 2025
OrdShap: Feature Position Importance for Sequential Black-Box Models
Davin Hill
Brian L. Hill
A. Masoomi
Vijay S. Nori
Robert E. Tillman
Jennifer Dy
FAtt
362
0
0
16 Jul 2025
TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift
Dipesh Tharu Mahato
Rohan Poudel
Pramod Dhungana
AAML
230
0
0
17 Jun 2025
Rethinking Explainability in the Era of Multimodal AI
Chirag Agarwal
304
3
0
16 Jun 2025
Local MDI+: Local Feature Importances for Tree-Based Models
Zhongyuan Liang
Zachary T. Rewolinski
Abhineet Agarwal
Tiffany M. Tang
Bin Yu
195
0
0
10 Jun 2025
XAI-Units: Benchmarking Explainability Methods with Unit Tests
Conference on Fairness, Accountability and Transparency (FAccT), 2025
Jun Rui Lee
Sadegh Emami
Michael David Hollins
Timothy C. H. Wong
Carlos Ignacio Villalobos Sánchez
Francesca Toni
Dekai Zhang
Adam Dejl
235
4
0
01 Jun 2025
A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations
Lingjun Zhao
Hal Daumé III
479
2
0
25 May 2025
Fixed Point Explainability
Emanuele La Malfa
Jon Vadillo
Marco Molinari
Michael Wooldridge
475
0
0
18 May 2025
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques
Lirandë Pira
Airin Antony
Nayanthara Prathap
Daniel Peace
Jacquiline Romero
397
0
0
14 May 2025
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Conference on Fairness, Accountability and Transparency (FAccT), 2025
Mahdi Dhaini
Ege Erdogan
Nils Feldhus
Gjergji Kasneci
400
1
0
02 May 2025
Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding
Simone Piaggesi
Riccardo Guidotti
F. Giannotti
D. Pedreschi
FAtt
MILM
LRM
1.2K
0
0
29 Apr 2025
Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations
Zhen Tan
Song Wang
Jiayi Zhang
Yu Kong
Jundong Li
Tianlong Chen
Huan Liu
FAtt
384
1
0
11 Apr 2025
Axiomatic Explainer Globalness via Optimal Transport
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Davin Hill
Josh Bone
A. Masoomi
Max Torop
Jennifer Dy
555
2
0
13 Mar 2025
Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures
Adaptive Agents and Multi-Agent Systems (AAMAS), 2025
Erfaun Noorani
Pasan Dissanayake
Faisal Hamman
Sanghamitra Dutta
300
3
0
11 Mar 2025
Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
International Conference on Human Factors in Computing Systems (CHI), 2025
Greta Warren
Irina Shklovski
Isabelle Augenstein
OffRL
876
34
0
13 Feb 2025
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models
Célia Wafa Ayad
Thomas Bonnier
Benjamin Bosch
Sonali Parbhoo
Jesse Read
FAtt
XAI
451
1
0
11 Feb 2025
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI
Christopher Burger
Charles Walter
Thai Le
AAML
421
3
0
20 Jan 2025
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI
Christopher Burger
Charles Walter
Thai Le
Lingwei Chen
AAML
308
1
0
03 Jan 2025
Q-LIME
π
π
π
: A Quantum-Inspired Extension to LIME
Nelson Colón Vargas
FAtt
248
2
0
23 Dec 2024
Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation
ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2024
Davor Vukadin
Petar Afrić
Marin Šilić
Goran Delač
FAtt
303
2
0
12 Dec 2024
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications
M. Islam
M. F. Mridha
Md Abrar Jahin
Nilanjan Dey
285
7
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Establishing and Evaluating Trustworthy AI: Overview and Research Challenges
Dominik Kowald
S. Scher
Viktoria Pammer-Schindler
Peter Müllner
Kerstin Waxnegger
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Andreas Truegler
Eduardo E. Veas
Roman Kern
Tomislav Nad
Simone Kopeinik
293
33
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15 Nov 2024
Benchmarking XAI Explanations with Human-Aligned Evaluations
Rémi Kazmierczak
Steve Azzolin
Eloise Berthier
Anna Hedström
Patricia Delhomme
...
Goran Frehse
Baptiste Caramiaux
Baptiste Caramiaux
Andrea Passerini
Gianni Franchi
511
5
0
04 Nov 2024
Transparent Trade-offs between Properties of Explanations
Conference on Uncertainty in Artificial Intelligence (UAI), 2024
Hiwot Belay Tadesse
Alihan Hüyük
Yaniv Yacoby
Weiwei Pan
Finale Doshi-Velez
FAtt
448
0
0
31 Oct 2024
Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences
Anushka Narayanan
Karianne J. Bergen
356
9
0
22 Oct 2024
A mechanistically interpretable neural network for regulatory genomics
Alex Tseng
Gökçen Eraslan
Tommaso Biancalani
Gabriele Scalia
173
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0
08 Oct 2024
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations
BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackBoxNLP), 2024
Supriya Manna
Niladri Sett
AAML
398
3
0
26 Sep 2024
A Fuzzy-based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy
IEEE transactions on fuzzy systems (IEEE Trans. Fuzzy Syst.), 2024
Xiaowei Jiang
Liang Ou
Yanan Chen
Na Ao
Yu-Cheng Chang
T. Do
Chin-Teng Lin
326
1
0
26 Sep 2024
The FIX Benchmark: Extracting Features Interpretable to eXperts
Helen Jin
Shreya Havaldar
Chaehyeon Kim
Anton Xue
Weiqiu You
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Amin Madani
M. Sako
Lyle Ungar
Eric Wong
445
4
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20 Sep 2024
Aligning Judgment Using Task Context and Explanations to Improve Human-Recommender System Performance
Divya K. Srivastava
Karen Feigh
161
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0
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Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
Ai & Society (AS), 2024
Andrew Smart
Atoosa Kasirzadeh
318
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Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy
IEEE Access (IEEE Access), 2024
Kimji N. Pellano
Inga Strümke
Daniel Groos
Lars Adde
Espen Alexander F. Ihlen
198
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0
14 Aug 2024
More Questions than Answers? Lessons from Integrating Explainable AI into a Cyber-AI Tool
Ashley Suh
Harry Li
Caitlin Kenney
Kenneth Alperin
Steven R. Gomez
AAML
201
4
0
08 Aug 2024
BEExAI: Benchmark to Evaluate Explainable AI
Samuel Sithakoul
Sara Meftah
Clément Feutry
432
17
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29 Jul 2024
Revisiting the robustness of post-hoc interpretability methods
Jiawen Wei
Hugues Turbé
G. Mengaldo
AAML
477
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Auditing Local Explanations is Hard
Robi Bhattacharjee
U. V. Luxburg
LRM
MLAU
FAtt
318
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Robustness of Explainable Artificial Intelligence in Industrial Process Modelling
Benedikt Kantz
Clemens Staudinger
C. Feilmayr
Johannes Wachlmayr
Alexander Haberl
Stefan Schuster
Franz Pernkopf
255
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12 Jul 2024
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