Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
1910.10045
Cited By
v1
v2 (latest)
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
22 October 2019
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
Siham Tabik
A. Barbado
S. García
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI"
50 / 1,389 papers shown
Title
Convolutional Neural Networks from Image Markers
B. C. Benato
I. E. D. Souza
F. L. Galvão
A. X. Falcão
13
4
0
15 Dec 2020
Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning
Georgios Th. Papadopoulos
M. Antona
C. Stephanidis
AI4CE
66
26
0
15 Dec 2020
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
Kashif Ahmad
Majdi Maabreh
M. Ghaly
Khalil Khan
Junaid Qadir
Ala I. Al-Fuqaha
121
157
0
14 Dec 2020
Evolutionary learning of interpretable decision trees
Leonardo Lucio Custode
Giovanni Iacca
OffRL
102
41
0
14 Dec 2020
Explanation from Specification
Harish Naik
Gyorgy Turán
XAI
47
0
0
13 Dec 2020
Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing
Yaojie Liu
Xiaoming Liu
AAML
115
10
0
09 Dec 2020
Explainable AI for Interpretable Credit Scoring
Lara Marie Demajo
Vince Vella
A. Dingli
77
39
0
03 Dec 2020
Self-Explaining Structures Improve NLP Models
Zijun Sun
Chun Fan
Qinghong Han
Xiaofei Sun
Yuxian Meng
Leilei Gan
Jiwei Li
MILM
XAI
LRM
FAtt
117
38
0
03 Dec 2020
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Eric L. Manibardo
I. Laña
Javier Del Ser
AI4TS
69
71
0
02 Dec 2020
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
Andrei Margeloiu
Nikola Simidjievski
M. Jamnik
Adrian Weller
GAN
AAML
MedIm
FAtt
52
18
0
02 Dec 2020
Reviewing the Need for Explainable Artificial Intelligence (xAI)
Julie Gerlings
Arisa Shollo
Ioanna D. Constantiou
61
73
0
02 Dec 2020
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment
Alfonso Ortega
Julian Fierrez
Aythami Morales
Zilong Wang
Tony Ribeiro
131
13
0
01 Dec 2020
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation
hsan Ullah
André Ríos
Vaibhav Gala
Susan Mckeever
FAtt
73
10
0
26 Nov 2020
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems
Adriano Lucieri
Muhammad Naseer Bajwa
Andreas Dengel
Sheraz Ahmed
131
10
0
26 Nov 2020
Quantifying Explainers of Graph Neural Networks in Computational Pathology
Guillaume Jaume
Pushpak Pati
Behzad Bozorgtabar
Antonio Foncubierta-Rodríguez
Florinda Feroce
A. Anniciello
T. Rau
Jean-Philippe Thiran
M. Gabrani
O. Goksel
FAtt
102
78
0
25 Nov 2020
PSD2 Explainable AI Model for Credit Scoring
N. Torrent
Giorgio Visani
Engineering
31
1
0
20 Nov 2020
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science
Adam J. Johs
Denise E. Agosto
Rosina O. Weber
58
6
0
13 Nov 2020
Unsupervised Explanation Generation for Machine Reading Comprehension
Yiming Cui
Ting Liu
Shijin Wang
Guoping Hu
LRM
30
3
0
13 Nov 2020
Interpretable collaborative data analysis on distributed data
A. Imakura
Hiroaki Inaba
Yukihiko Okada
Tetsuya Sakurai
FedML
38
26
0
09 Nov 2020
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
Agus Sudjianto
William Knauth
Rahul Singh
Zebin Yang
Aijun Zhang
FAtt
69
46
0
08 Nov 2020
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
Meike Nauta
Annemarie Jutte
Jesper C. Provoost
C. Seifert
FAtt
111
65
0
05 Nov 2020
Towards Personalized Explanation of Robot Path Planning via User Feedback
Kayla Boggess
Shenghui Chen
Lu Feng
40
1
0
01 Nov 2020
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies
A. Barbado
Óscar Corcho
24
11
0
28 Oct 2020
Now You See Me (CME): Concept-based Model Extraction
Dmitry Kazhdan
B. Dimanov
M. Jamnik
Pietro Lio
Adrian Weller
56
75
0
25 Oct 2020
Abduction and Argumentation for Explainable Machine Learning: A Position Survey
A. Kakas
Loizos Michael
29
11
0
24 Oct 2020
Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams
Dave Braines
Federico Cerutti
Marc Roig Vilamala
Mani B. Srivastava
Alun D. Preece
G. Pearson
44
4
0
23 Oct 2020
Model Interpretability through the Lens of Computational Complexity
Pablo Barceló
Mikaël Monet
Jorge A. Pérez
Bernardo Subercaseaux
212
98
0
23 Oct 2020
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains
Eyal Shnarch
Leshem Choshen
Guy Moshkowich
Noam Slonim
R. Aharonov
155
11
0
19 Oct 2020
A Framework to Learn with Interpretation
Jayneel Parekh
Pavlo Mozharovskyi
Florence dÁlché-Buc
AI4CE
FAtt
80
30
0
19 Oct 2020
Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic
Daniel Zeltner
Benedikt Schmid
G. Csiszár
O. Csiszár
AAML
13
16
0
17 Oct 2020
Physics-informed GANs for Coastal Flood Visualization
Björn Lütjens
B. Leshchinskiy
C. Requena-Mesa
F. Chishtie
Natalia Díaz Rodríguez
...
A. Piña
Dava Newman
Alexander Lavin
Y. Gal
Chedy Raïssi
AI4CE
48
15
0
16 Oct 2020
Do's and Don'ts for Human and Digital Worker Integration
Vinod Muthusamy
Merve Unuvar
Hagen Volzer
Justin D. Weisz
39
2
0
15 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
A. Konstantinov
Lev V. Utkin
FedML
AI4CE
60
153
0
14 Oct 2020
Adaptive Deep Forest for Online Learning from Drifting Data Streams
Lukasz Korycki
Bartosz Krawczyk
27
4
0
14 Oct 2020
Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction
Tom Weber
S. Wermter
25
4
0
09 Oct 2020
Sickle-cell disease diagnosis support selecting the most appropriate machinelearning method: Towards a general and interpretable approach for cellmorphology analysis from microscopy images
N. Petrovic
Gabriel Moyà-Alcover
Antoni Jaume-i-Capó
Manuel González Hidalgo
35
36
0
09 Oct 2020
Association rules over time
Iztok Fister
Iztok Fister
AI4TS
26
4
0
08 Oct 2020
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification
Florentin Flambeau Jiechieu Kameni
Norbert Tsopzé
XAI
FAtt
MedIm
26
1
0
08 Oct 2020
Explaining Deep Neural Networks
Oana-Maria Camburu
XAI
FAtt
108
26
0
04 Oct 2020
Explanation Ontology: A Model of Explanations for User-Centered AI
Shruthi Chari
Oshani Seneviratne
Daniel Gruen
Morgan Foreman
Amar K. Das
D. McGuinness
XAI
46
55
0
04 Oct 2020
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
S. Sattarzadeh
M. Sudhakar
Anthony Lem
Shervin Mehryar
K. N. Plataniotis
Jongseong Jang
Hyunwoo J. Kim
Yeonjeong Jeong
Sang-Min Lee
Kyunghoon Bae
FAtt
XAI
55
33
0
01 Oct 2020
Explainable Deep Reinforcement Learning for UAV Autonomous Navigation
Lei He
Nabil Aouf
Bifeng Song
58
11
0
30 Sep 2020
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection
Bernat Coma-Puig
J. Carmona
43
1
0
28 Sep 2020
A light-weight method to foster the (Grad)CAM interpretability and explainability of classification networks
Alfred Schöttl
FAtt
19
9
0
26 Sep 2020
A Diagnostic Study of Explainability Techniques for Text Classification
Pepa Atanasova
J. Simonsen
Christina Lioma
Isabelle Augenstein
XAI
FAtt
101
226
0
25 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors
Yi-Shan Lin
Wen-Chuan Lee
Z. Berkay Celik
XAI
102
97
0
22 Sep 2020
CURIE: A Cellular Automaton for Concept Drift Detection
J. Lobo
Javier Del Ser
E. Osaba
Albert Bifet
Francisco Herrera
AI4TS
37
7
0
21 Sep 2020
Principles and Practice of Explainable Machine Learning
Vaishak Belle
I. Papantonis
FaML
84
454
0
18 Sep 2020
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
197
80
0
17 Sep 2020
MeLIME: Meaningful Local Explanation for Machine Learning Models
T. Botari
Frederik Hvilshoj
Rafael Izbicki
A. Carvalho
AAML
FAtt
75
16
0
12 Sep 2020
Previous
1
2
3
...
25
26
27
28
Next