Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

J. M. Górriz; I. Álvarez-Illán; A. Álvarez-Marquina; J. E. Arco; M. Atzmueller; F. Ballarini; E. Barakova; G. Bologna; P. Bonomini; G. Castellanos-Dominguez; D. Castillo-Barnes; S. B. Cho; R. Contreras; J. M. Cuadra; E. Domínguez; F. Domínguez-Mateos; R. J. Duro; D. Elizondo; A. Fernández-Caballero; E. Fernandez-Jover; M. A. Formoso; N. J. Gallego-Molina; J. Gamazo; J. García González; J. Garcia-Rodriguez; C. Garre; J. Garrigós; A. Gómez-Rodellar; P. Gómez-Vilda; M. Graña; B. Guerrero-Rodriguez; S. C.F. Hendrikse; C. Jimenez-Mesa; M. Jodra-Chuan; V. Julian; G. Kotz; K. Kutt; M. Leming; J. de Lope; B. Macas; V. Marrero-Aguiar; J. J. Martinez; F. J. Martinez-Murcia; R. Martínez-Tomás; J. Mekyska; G. J. Nalepa; P. Novais; D. Orellana; A. Ortiz; D. Palacios-Alonso; J. Palma; A. Pereira; P. Pinacho-Davidson; M. A. Pinninghoff; M. Ponticorvo; A. Psarrou; J. Ramírez; M. Rincón; V. Rodellar-Biarge; I. Rodríguez-Rodríguez; P. H.M.P. Roelofsma; J. Santos; D. Salas-Gonzalez; P. Salcedo-Lagos; F. Segovia; A. Shoeibi; M. Silva; D. Simic; J. Suckling; J. Treur; A. Tsanas; R. Varela; S. H. Wang; W. Wang; Y. D. Zhang; H. Zhu; Z. Zhu; J. M. Ferrández-Vicente

doi:10.1016/j.inffus.2023.101945

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

J. M. Górriz^*, I. Álvarez-Illán, A. Álvarez-Marquina, J. E. Arco, M. Atzmueller, F. Ballarini, E. Barakova, G. Bologna, P. Bonomini, G. Castellanos-Dominguez, D. Castillo-Barnes, S. B. Cho, R. Contreras, J. M. Cuadra, E. Domínguez, F. Domínguez-Mateos, R. J. Duro, D. Elizondo, A. Fernández-Caballero, E. Fernandez-JoverM. A. Formoso, N. J. Gallego-Molina, J. Gamazo, J. García González, J. Garcia-Rodriguez, C. Garre, J. Garrigós, A. Gómez-Rodellar, P. Gómez-Vilda, M. Graña, B. Guerrero-Rodriguez, S. C.F. Hendrikse, C. Jimenez-Mesa, M. Jodra-Chuan, V. Julian, G. Kotz, K. Kutt, M. Leming, J. de Lope, B. Macas, V. Marrero-Aguiar, J. J. Martinez, F. J. Martinez-Murcia, R. Martínez-Tomás, J. Mekyska, G. J. Nalepa, P. Novais, D. Orellana, A. Ortiz, D. Palacios-Alonso, J. Palma, A. Pereira, P. Pinacho-Davidson, M. A. Pinninghoff, M. Ponticorvo, A. Psarrou, J. Ramírez, M. Rincón, V. Rodellar-Biarge, I. Rodríguez-Rodríguez, P. H.M.P. Roelofsma, J. Santos, D. Salas-Gonzalez, P. Salcedo-Lagos, F. Segovia, A. Shoeibi, M. Silva, D. Simic, J. Suckling, J. Treur, A. Tsanas, R. Varela, S. H. Wang, W. Wang, Y. D. Zhang, H. Zhu, Z. Zhu, J. M. Ferrández-Vicente

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

Original language	English
Article number	101945
Pages (from-to)	1-37
Number of pages	37
Journal	Information Fusion
Volume	100
Early online date	29 Jul 2023
DOIs	https://doi.org/10.1016/j.inffus.2023.101945
Publication status	Published - Dec 2023

Bibliographical note

Funding Information:
Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00 .

Funding Information:
This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 ( Spanish “Ministerio de Ciencia, Innovacón y Universidades” ), P18-RT-1624 , UMA20-FEDERJA-086 , CV20-45250 , A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF) . M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”.

Funding Information:
José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC ( ED431G 2019/01 ), GPC ED431B 2022/33 , and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00 ). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID).

Funding Information:
The work reported here has been partially funded by Project Fondecyt 1201572 (ANID).

Funding Information:
The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084 , project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb.

Funding Information:
In [247] , the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain) . In [248] , the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4 , 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies” BG05M2OP001-1.002-0023 .

Funding Information:
This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746 ). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058 .

Funding Information:
Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 , A-TIC-080-UGR18 , B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes.

Funding Information:
The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III ; by MinCiencias project 1222-852-69927 , contract 495-2020 .

Funding Information:
The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019 .

Funding Information:
S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361 , Artificial Intelligence Graduate School Program (Yonsei University)).

Funding Information:
The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe” , by the “European Union” or by the “European Union NextGenerationEU/PRTR” .

Publisher Copyright:
© 2023 The Author(s)

Funding

Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00 . This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 ( Spanish “Ministerio de Ciencia, Innovacón y Universidades” ), P18-RT-1624 , UMA20-FEDERJA-086 , CV20-45250 , A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF) . M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”. José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC ( ED431G 2019/01 ), GPC ED431B 2022/33 , and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00 ). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084 , project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb. In [247] , the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain) . In [248] , the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4 , 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies” BG05M2OP001-1.002-0023 . This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746 ). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058 . Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 , A-TIC-080-UGR18 , B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes. The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III ; by MinCiencias project 1222-852-69927 , contract 495-2020 . The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019 . S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361 , Artificial Intelligence Graduate School Program (Yonsei University)). The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe” , by the “European Union” or by the “European Union NextGenerationEU/PRTR” .

Funders	Funder number
Artificial Intelligence Graduate School Program
Autonomous Government of Andalusia (Spain)	UMA18-FEDERJA-084
CBUA
Consejería de econnomía y conocimiento
European Commission Horizon Europe Program	101057746
Ministerio de Universidades, Gobierno de España	PID2020-115220RB-C22
Spanish State Agency for Research
Nvidia
Ministerio de Ciencia, Innovación y Universidades	P18-RT-1624, PROMETEO/2019/119, P20 00525, UMA20-FEDERJA-086
Ministerio de Ciencia, Innovación y Universidades
Ministerio de Ciencia, Tecnología e Innovación	1222-852-69927, 495-2020
Ministerio de Ciencia, Tecnología e Innovación
European Commission	777720, BG05M2OP001-1.002-0023, H2020-MSCA-RISE-2017
European Commission
Fundação para a Ciência e a Tecnologia	DSAIPA/AI/0099/2019
Fundação para a Ciência e a Tecnologia
Yonsei University
Fondo Nacional de Desarrollo Científico y Tecnológico	1201572
Fondo Nacional de Desarrollo Científico y Tecnológico
Bulgarian National Science Fund	KP-06-N42/4, 08.12.2020
Bulgarian National Science Fund
Generalitat Valenciana
Ministry of Science, ICT and Future Planning	2020-0-01361
Ministry of Science, ICT and Future Planning
Instituto de Salud Carlos III
Ministerio de Ciencia e Innovación	PID2020-116201GB-I00
Ministerio de Ciencia e Innovación
European Social Fund	FPU16/03740
European Social Fund
Universidad de Granada
Centro de Investigación Biomédica en Red de Salud Mental
Staatssekretariat für Bildung, Forschung und Innovation	22 00058
Staatssekretariat für Bildung, Forschung und Innovation
European Regional Development Fund	A-TIC-080-UGR18, MCIN/AEI/10.13039/501100011033, B-TIC-586-UGR20, EQC2019-006063-P, P20-00525, GPC ED431B 2022/33, PRE2019-087350, ED431G 2019/01, PID2020-115220RB-C21, CV20-45250, RTI2018-098913-B100
European Regional Development Fund
Institute for Information and Communications Technology Promotion
Xunta de Galicia
Junta de Andalucía
Agencia Estatal de Investigación	PID2019-106263RB-I00
Agencia Estatal de Investigación
Agencia Nacional de Investigación y Desarrollo	RTI2018-098969-B-100
Agencia Nacional de Investigación y Desarrollo
Ministerio de Universidades	PGC2018-098813-B-C32, FPU18/04902
Ministerio de Universidades

Keywords

Biomedical applications
Computational approaches
Computer-aided diagnosis systems
Data science
Deep learning
Explainable Artificial Intelligence
Machine learning
Neuroscience
Robotics

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10.1016/j.inffus.2023.101945

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Górriz, J. M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J. E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S. B., Contreras, R., Cuadra, J. M., Domínguez, E., Domínguez-Mateos, F., Duro, R. J., Elizondo, D., Fernández-Caballero, A., ... Ferrández-Vicente, J. M. (2023). Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends. Information Fusion, 100, 1-37. Article 101945. https://doi.org/10.1016/j.inffus.2023.101945

@article{bd4d9305bc1e4fe6ab8c998c5a2a28aa,

title = "Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends",

abstract = "Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.",

keywords = "Biomedical applications, Computational approaches, Computer-aided diagnosis systems, Data science, Deep learning, Explainable Artificial Intelligence, Machine learning, Neuroscience, Robotics",

author = "G{\'o}rriz, {J. M.} and I. {\'A}lvarez-Ill{\'a}n and A. {\'A}lvarez-Marquina and Arco, {J. E.} and M. Atzmueller and F. Ballarini and E. Barakova and G. Bologna and P. Bonomini and G. Castellanos-Dominguez and D. Castillo-Barnes and Cho, {S. B.} and R. Contreras and Cuadra, {J. M.} and E. Dom{\'i}nguez and F. Dom{\'i}nguez-Mateos and Duro, {R. J.} and D. Elizondo and A. Fern{\'a}ndez-Caballero and E. Fernandez-Jover and Formoso, {M. A.} and Gallego-Molina, {N. J.} and J. Gamazo and Gonz{\'a}lez, {J. Garc{\'i}a} and J. Garcia-Rodriguez and C. Garre and J. Garrig{\'o}s and A. G{\'o}mez-Rodellar and P. G{\'o}mez-Vilda and M. Gra{\~n}a and B. Guerrero-Rodriguez and Hendrikse, {S. C.F.} and C. Jimenez-Mesa and M. Jodra-Chuan and V. Julian and G. Kotz and K. Kutt and M. Leming and {de Lope}, J. and B. Macas and V. Marrero-Aguiar and Martinez, {J. J.} and Martinez-Murcia, {F. J.} and R. Mart{\'i}nez-Tom{\'a}s and J. Mekyska and Nalepa, {G. J.} and P. Novais and D. Orellana and A. Ortiz and D. Palacios-Alonso and J. Palma and A. Pereira and P. Pinacho-Davidson and Pinninghoff, {M. A.} and M. Ponticorvo and A. Psarrou and J. Ram{\'i}rez and M. Rinc{\'o}n and V. Rodellar-Biarge and I. Rodr{\'i}guez-Rodr{\'i}guez and Roelofsma, {P. H.M.P.} and J. Santos and D. Salas-Gonzalez and P. Salcedo-Lagos and F. Segovia and A. Shoeibi and M. Silva and D. Simic and J. Suckling and J. Treur and A. Tsanas and R. Varela and Wang, {S. H.} and W. Wang and Zhang, {Y. D.} and H. Zhu and Z. Zhu and Ferr{\'a}ndez-Vicente, {J. M.}",

note = "Funding Information: Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00 . Funding Information: This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 ( Spanish “Ministerio de Ciencia, Innovac{\'o}n y Universidades” ), P18-RT-1624 , UMA20-FEDERJA-086 , CV20-45250 , A-TIC-080-UGR18 and P20 00525 (Consejer{\'i}a de econnom{\'i}a y conocimiento, Junta de Andaluc{\'i}a) and by European Regional Development Funds (ERDF) . M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de Espa{\~n}a through grant “Margarita Salas”. Funding Information: Jos{\'e} Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC ( ED431G 2019/01 ), GPC ED431B 2022/33 , and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00 ). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). Funding Information: The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). Funding Information: The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084 , project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb. Funding Information: In [247] , the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovaci{\'o}n y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain) . In [248] , the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4 , 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies” BG05M2OP001-1.002-0023 . Funding Information: This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746 ). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058 . Funding Information: Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 , A-TIC-080-UGR18 , B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes. Funding Information: The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III ; by MinCiencias project 1222-852-69927 , contract 495-2020 . Funding Information: The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Funda{\c c}{\H a}o para a Ci{\^e}ncia e a Tecnologia within project DSAIPA/AI/0099/2019 . Funding Information: S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361 , Artificial Intelligence Graduate School Program (Yonsei University)). Funding Information: The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe” , by the “European Union” or by the “European Union NextGenerationEU/PRTR” . Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = dec,

doi = "10.1016/j.inffus.2023.101945",

language = "English",

volume = "100",

pages = "1--37",

journal = "Information Fusion",

issn = "1566-2535",

publisher = "Elsevier",

}

Górriz, JM, Álvarez-Illán, I, Álvarez-Marquina, A, Arco, JE, Atzmueller, M, Ballarini, F, Barakova, E, Bologna, G, Bonomini, P, Castellanos-Dominguez, G, Castillo-Barnes, D, Cho, SB, Contreras, R, Cuadra, JM, Domínguez, E, Domínguez-Mateos, F, Duro, RJ, Elizondo, D, Fernández-Caballero, A, Fernandez-Jover, E, Formoso, MA, Gallego-Molina, NJ, Gamazo, J, González, JG, Garcia-Rodriguez, J, Garre, C, Garrigós, J, Gómez-Rodellar, A, Gómez-Vilda, P, Graña, M, Guerrero-Rodriguez, B, Hendrikse, SCF, Jimenez-Mesa, C, Jodra-Chuan, M, Julian, V, Kotz, G, Kutt, K, Leming, M, de Lope, J, Macas, B, Marrero-Aguiar, V, Martinez, JJ, Martinez-Murcia, FJ, Martínez-Tomás, R, Mekyska, J, Nalepa, GJ, Novais, P, Orellana, D, Ortiz, A, Palacios-Alonso, D, Palma, J, Pereira, A, Pinacho-Davidson, P, Pinninghoff, MA, Ponticorvo, M, Psarrou, A, Ramírez, J, Rincón, M, Rodellar-Biarge, V, Rodríguez-Rodríguez, I, Roelofsma, PHMP, Santos, J, Salas-Gonzalez, D, Salcedo-Lagos, P, Segovia, F, Shoeibi, A, Silva, M, Simic, D, Suckling, J, Treur, J, Tsanas, A, Varela, R, Wang, SH, Wang, W, Zhang, YD, Zhu, H, Zhu, Z & Ferrández-Vicente, JM 2023, 'Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends', Information Fusion, vol. 100, 101945, pp. 1-37. https://doi.org/10.1016/j.inffus.2023.101945

TY - JOUR

T1 - Computational approaches to Explainable Artificial Intelligence

T2 - Advances in theory, applications and trends

AU - Górriz, J. M.

AU - Álvarez-Illán, I.

AU - Álvarez-Marquina, A.

AU - Arco, J. E.

AU - Atzmueller, M.

AU - Ballarini, F.

AU - Barakova, E.

AU - Bologna, G.

AU - Bonomini, P.

AU - Castellanos-Dominguez, G.

AU - Castillo-Barnes, D.

AU - Cho, S. B.

AU - Contreras, R.

AU - Cuadra, J. M.

AU - Domínguez, E.

AU - Domínguez-Mateos, F.

AU - Duro, R. J.

AU - Elizondo, D.

AU - Fernández-Caballero, A.

AU - Fernandez-Jover, E.

AU - Formoso, M. A.

AU - Gallego-Molina, N. J.

AU - Gamazo, J.

AU - González, J. García

AU - Garcia-Rodriguez, J.

AU - Garre, C.

AU - Garrigós, J.

AU - Gómez-Rodellar, A.

AU - Gómez-Vilda, P.

AU - Graña, M.

AU - Guerrero-Rodriguez, B.

AU - Hendrikse, S. C.F.

AU - Jimenez-Mesa, C.

AU - Jodra-Chuan, M.

AU - Julian, V.

AU - Kotz, G.

AU - Kutt, K.

AU - Leming, M.

AU - de Lope, J.

AU - Macas, B.

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AU - Martinez, J. J.

AU - Martinez-Murcia, F. J.

AU - Martínez-Tomás, R.

AU - Mekyska, J.

AU - Nalepa, G. J.

AU - Novais, P.

AU - Orellana, D.

AU - Ortiz, A.

AU - Palacios-Alonso, D.

AU - Palma, J.

AU - Pereira, A.

AU - Pinacho-Davidson, P.

AU - Pinninghoff, M. A.

AU - Ponticorvo, M.

AU - Psarrou, A.

AU - Ramírez, J.

AU - Rincón, M.

AU - Rodellar-Biarge, V.

AU - Rodríguez-Rodríguez, I.

AU - Roelofsma, P. H.M.P.

AU - Santos, J.

AU - Salas-Gonzalez, D.

AU - Salcedo-Lagos, P.

AU - Segovia, F.

AU - Shoeibi, A.

AU - Silva, M.

AU - Simic, D.

AU - Suckling, J.

AU - Treur, J.

AU - Tsanas, A.

AU - Varela, R.

AU - Wang, S. H.

AU - Wang, W.

AU - Zhang, Y. D.

AU - Zhu, H.

AU - Zhu, Z.

AU - Ferrández-Vicente, J. M.

N1 - Funding Information: Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00 . Funding Information: This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 ( Spanish “Ministerio de Ciencia, Innovacón y Universidades” ), P18-RT-1624 , UMA20-FEDERJA-086 , CV20-45250 , A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF) . M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”. Funding Information: José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC ( ED431G 2019/01 ), GPC ED431B 2022/33 , and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00 ). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). Funding Information: The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). Funding Information: The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084 , project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb. Funding Information: In [247] , the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain) . In [248] , the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4 , 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies” BG05M2OP001-1.002-0023 . Funding Information: This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746 ). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058 . Funding Information: Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 , A-TIC-080-UGR18 , B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes. Funding Information: The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III ; by MinCiencias project 1222-852-69927 , contract 495-2020 . Funding Information: The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019 . Funding Information: S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361 , Artificial Intelligence Graduate School Program (Yonsei University)). Funding Information: The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe” , by the “European Union” or by the “European Union NextGenerationEU/PRTR” . Publisher Copyright: © 2023 The Author(s)

PY - 2023/12

Y1 - 2023/12

N2 - Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

AB - Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

KW - Biomedical applications

KW - Computational approaches

KW - Computer-aided diagnosis systems

KW - Data science

KW - Deep learning

KW - Explainable Artificial Intelligence

KW - Machine learning

KW - Neuroscience

KW - Robotics

UR - http://www.scopus.com/inward/record.url?scp=85166914338&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85166914338&partnerID=8YFLogxK

U2 - 10.1016/j.inffus.2023.101945

DO - 10.1016/j.inffus.2023.101945

M3 - Article

AN - SCOPUS:85166914338

SN - 1566-2535

VL - 100

SP - 1

EP - 37

JO - Information Fusion

JF - Information Fusion

M1 - 101945

ER -

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

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