ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1910.10045
  4. Cited By
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies,
  Opportunities and Challenges toward Responsible AI
v1v2 (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
ArXiv (abs)PDFHTML

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
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
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
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
Evolutionary learning of interpretable decision trees
Leonardo Lucio Custode
Giovanni Iacca
OffRL
102
41
0
14 Dec 2020
Explanation from Specification
Explanation from Specification
Harish Naik
Gyorgy Turán
XAI
47
0
0
13 Dec 2020
Physics-Guided Spoof Trace Disentanglement for Generic Face
  Anti-Spoofing
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
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
Self-Explaining Structures Improve NLP Models
Zijun Sun
Chun Fan
Qinghong Han
Xiaofei Sun
Yuxian Meng
Leilei Gan
Jiwei Li
MILMXAILRMFAtt
117
38
0
03 Dec 2020
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
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
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
Andrei Margeloiu
Nikola Simidjievski
M. Jamnik
Adrian Weller
GANAAMLMedImFAtt
52
18
0
02 Dec 2020
Reviewing the Need for Explainable Artificial Intelligence (xAI)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
A Framework to Learn with Interpretation
Jayneel Parekh
Pavlo Mozharovskyi
Florence dÁlché-Buc
AI4CEFAtt
80
30
0
19 Oct 2020
Squashing activation functions in benchmark tests: towards eXplainable
  Artificial Intelligence using continuous-valued logic
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
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
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
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
A. Konstantinov
Lev V. Utkin
FedMLAI4CE
60
153
0
14 Oct 2020
Adaptive Deep Forest for Online Learning from Drifting Data Streams
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
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
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
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
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification
Florentin Flambeau Jiechieu Kameni
Norbert Tsopzé
XAIFAttMedIm
26
1
0
08 Oct 2020
Explaining Deep Neural Networks
Explaining Deep Neural Networks
Oana-Maria Camburu
XAIFAtt
108
26
0
04 Oct 2020
Explanation Ontology: A Model of Explanations for User-Centered AI
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
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
FAttXAI
55
33
0
01 Oct 2020
Explainable Deep Reinforcement Learning for UAV Autonomous Navigation
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
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
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
A Diagnostic Study of Explainability Techniques for Text Classification
Pepa Atanasova
J. Simonsen
Christina Lioma
Isabelle Augenstein
XAIFAtt
101
226
0
25 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI)
  Interpretability through Neural Backdoors
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
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
Principles and Practice of Explainable Machine Learning
Vaishak Belle
I. Papantonis
FaML
84
454
0
18 Sep 2020
Review: Deep Learning in Electron Microscopy
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
197
80
0
17 Sep 2020
MeLIME: Meaningful Local Explanation for Machine Learning Models
MeLIME: Meaningful Local Explanation for Machine Learning Models
T. Botari
Frederik Hvilshoj
Rafael Izbicki
A. Carvalho
AAMLFAtt
75
16
0
12 Sep 2020
Previous
123...25262728
Next