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2001.09734
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One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency
27 January 2020
Kacper Sokol
Peter A. Flach
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Papers citing
"One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency"
49 / 49 papers shown
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
Yehonatan Elisha
Seffi Cohen
Oren Barkan
Noam Koenigstein
FAtt
390
3
0
17 Nov 2025
T-FIX: Text-Based Explanations with Features Interpretable to eXperts
Shreya Havaldar
Helen Jin
Chaehyeon Kim
Anton Xue
Weiqiu You
...
Rajat Deo
Sameed Ahmed M. Khatana
Gary E. Weissman
Lyle Ungar
Eric Wong
146
1
0
06 Nov 2025
Explainable AI the Latest Advancements and New Trends
Bowen Long
Enjie Liu
Renxi Qiu
Yanqing Duan
XAI
809
6
0
11 May 2025
All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty
Kacper Sokol
Eyke Hüllermeier
470
4
0
24 Feb 2025
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills
International Conference on Human Factors in Computing Systems (CHI), 2024
Zana Buçinca
S. Swaroop
Amanda E. Paluch
Finale Doshi-Velez
Krzysztof Z. Gajos
407
28
0
05 Oct 2024
Data Science Principles for Interpretable and Explainable AI
Journal of Data Science (JDS), 2024
Kris Sankaran
FaML
379
6
0
17 May 2024
Declarative Reasoning on Explanations Using Constraint Logic Programming
European Conference on Logics in Artificial Intelligence (JELIA), 2023
Laura State
Salvatore Ruggieri
Franco Turini
LRM
356
1
0
01 Sep 2023
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities
Xudong Shen
H. Brown
Jiashu Tao
Martin Strobel
Yao Tong
Akshay Narayan
Harold Soh
Finale Doshi-Velez
381
4
0
22 Jun 2023
Navigating Explanatory Multiverse Through Counterfactual Path Geometry
Machine-mediated learning (ML), 2023
Kacper Sokol
E. Small
Yueqing Xuan
488
7
0
05 Jun 2023
Reason to explain: Interactive contrastive explanations (REASONX)
Laura State
Salvatore Ruggieri
Franco Turini
LRM
386
3
0
29 May 2023
Visualization for Recommendation Explainability: A Survey and New Perspectives
Mohamed Amine Chatti
Mouadh Guesmi
Arham Muslim
XAI
HAI
LRM
289
25
0
19 May 2023
Trust and Transparency in Recommender Systems
Clara Siepmann
Mohamed Amine Chatti
193
6
0
17 Apr 2023
One Explanation Does Not Fit XIL
Felix Friedrich
David Steinmann
Kristian Kersting
LRM
345
3
0
14 Apr 2023
Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals
Greta Warren
Markt. Keane
Christophe Guéret
Eoin Delaney
254
15
0
16 Mar 2023
A Survey on Explainable Artificial Intelligence for Cybersecurity
IEEE Transactions on Network and Service Management (TNSM), 2023
Gaith Rjoub
Jamal Bentahar
Omar Abdel Wahab
R. Mizouni
Alyssa Song
Robin Cohen
Hadi Otrok
Azzam Mourad
382
76
0
07 Mar 2023
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations
E. Small
Yueqing Xuan
Danula Hettiachchi
Kacper Sokol
276
11
0
02 Mar 2023
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
B. Keenan
Kacper Sokol
442
9
0
07 Feb 2023
Behaviour Trees for Creating Conversational Explanation Experiences
A. Wijekoon
D. Corsar
Nirmalie Wiratunga
234
3
0
11 Nov 2022
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective
Expert systems with applications (ESWA), 2022
José de Sousa Ribeiro Filho
Lucas F. F. Cardoso
R. Silva
Vitor Cirilo Araujo Santos
Nikolas Carneiro
Ronnie Cley de Oliveira Alves
280
7
0
18 Oct 2022
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers
British Machine Vision Conference (BMVC), 2022
Ricardo Kleinlein
Alexander Hepburn
Raúl Santos-Rodríguez
Fernando Fernández-Martínez
AAML
FAtt
191
3
0
08 Aug 2022
Leveraging Explanations in Interactive Machine Learning: An Overview
Frontiers in Artificial Intelligence (FAI), 2022
Stefano Teso
Öznur Alkan
Wolfgang Stammer
Elizabeth M. Daly
XAI
FAtt
LRM
626
82
0
29 Jul 2022
Why we do need Explainable AI for Healthcare
Giovanni Cina
Tabea E. Rober
Rob Goedhart
Ilker Birbil
265
21
0
30 Jun 2022
Mediators: Conversational Agents Explaining NLP Model Behavior
Nils Feldhus
A. Ravichandran
Sebastian Möller
294
17
0
13 Jun 2022
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
Data & Policy (DP), 2022
Andrew Bell
O. Nov
Julia Stoyanovich
221
34
0
10 Jun 2022
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning
Ulrike Kuhl
André Artelt
Barbara Hammer
246
23
0
06 May 2022
Diagnosing AI Explanation Methods with Folk Concepts of Behavior
Conference on Fairness, Accountability and Transparency (FAccT), 2022
Alon Jacovi
Jasmijn Bastings
Sebastian Gehrmann
Yoav Goldberg
Katja Filippova
555
22
0
27 Jan 2022
Explainable Decision Making with Lean and Argumentative Explanations
Xiuyi Fan
Francesca Toni
290
1
0
18 Jan 2022
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence
Kacper Sokol
Peter A. Flach
291
28
0
29 Dec 2021
AI Ethics Principles in Practice: Perspectives of Designers and Developers
Conrad Sanderson
David M. Douglas
Qinghua Lu
Emma Schleiger
Jon Whittle
J. Lacey
G. Newnham
S. Hajkowicz
Cathy J. Robinson
David Hansen
FaML
549
77
0
14 Dec 2021
A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction
Marco Matarese
F. Rea
A. Sciutti
220
19
0
27 Sep 2021
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey
Richard Dazeley
Peter Vamplew
Francisco Cruz
294
78
0
20 Aug 2021
Explanatory Pluralism in Explainable AI
International Cross-Domain Conference on Machine Learning and Knowledge Extraction (CD-MAKE), 2021
Yiheng Yao
XAI
189
6
0
26 Jun 2021
It's FLAN time! Summing feature-wise latent representations for interpretability
An-phi Nguyen
María Rodríguez Martínez
FAtt
308
0
0
18 Jun 2021
Do not explain without context: addressing the blind spot of model explanations
Katarzyna Wo'znica
Katarzyna Pkekala
Hubert Baniecki
Wojciech Kretowicz
El.zbieta Sienkiewicz
P. Biecek
169
1
0
28 May 2021
Effects of interactivity and presentation on review-based explanations for recommendations
IFIP TC13 International Conference on Human-Computer Interaction (INTERACT), 2021
Diana C. Hernandez-Bocanegra
J. Ziegler
186
15
0
25 May 2021
Explainable Machine Learning with Prior Knowledge: An Overview
Katharina Beckh
Sebastian Müller
Matthias Jakobs
Vanessa Toborek
Hanxiao Tan
Raphael Fischer
Pascal Welke
Sebastian Houben
Laura von Rueden
XAI
340
32
0
21 May 2021
A Review on Explainability in Multimodal Deep Neural Nets
IEEE Access (IEEE Access), 2021
Gargi Joshi
Rahee Walambe
K. Kotecha
524
183
0
17 May 2021
A Conceptual Framework for Establishing Trust in Real World Intelligent Systems
Cognitive Systems Research (CSR), 2021
Michael Guckert
Nils Gumpfer
J. Hannig
Till Keller
N. Urquhart
228
3
0
12 Apr 2021
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception
International Conference on Information Photonics (ICIP), 2021
Alexander Hepburn
Raúl Santos-Rodríguez
FAtt
214
4
0
22 Feb 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
International Conference on Intelligent User Interfaces (IUI), 2021
Harini Suresh
Kathleen M. Lewis
John Guttag
Arvind Satyanarayan
FAtt
296
30
0
17 Feb 2021
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
Artificial Intelligence (AI), 2021
Markus Langer
Daniel Oster
Timo Speith
Holger Hermanns
Lena Kästner
Eva Schmidt
Andreas Sesing
Kevin Baum
XAI
401
523
0
15 Feb 2021
Directive Explanations for Actionable Explainability in Machine Learning Applications
Ronal Singh
Paul Dourish
Piers Howe
Tim Miller
L. Sonenberg
Eduardo Velloso
F. Vetere
232
44
0
03 Feb 2021
The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?
Thomas P. Quinn
Stephan Jacobs
M. Senadeera
Vuong Le
S. Coghlan
261
157
0
10 Dec 2020
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
Michael Bücker
G. Szepannek
Alicja Gosiewska
P. Biecek
FaML
270
163
0
28 Sep 2020
Explainable Empirical Risk Minimization
Linli Zhang
Georgios Karakasidis
Arina Odnoblyudova
Leyla Dogruel
Alex Jung
220
9
0
03 Sep 2020
Machine Reasoning Explainability
K. Čyras
R. Badrinath
S. Mohalik
A. Mujumdar
Alexandros Nikou
Alessandro Previti
Vaishnavi Sundararajan
Aneta Vulgarakis Feljan
LRM
391
13
0
01 Sep 2020
Interpretable Representations in Explainable AI: From Theory to Practice
Kacper Sokol
Peter A. Flach
391
16
0
16 Aug 2020
The Grammar of Interactive Explanatory Model Analysis
Data mining and knowledge discovery (DMKD), 2020
Hubert Baniecki
Dariusz Parzych
P. Biecek
488
55
0
01 May 2020
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations
Michal Kuzba
P. Biecek
HAI
205
25
0
07 Feb 2020
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