Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
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AuthorGorriz Sáez, Juan Manuel; Álvarez Illán, Ignacio; Arco Martín, Juan Eloy; Castillo Barnes, Diego; Formoso, Marco A.; Gallego Molina, Nicolás J.; Jiménez Mesa, Carmen; Martínez Murcia, Francisco Jesús; Ortiz García, Andrés; Ramírez Pérez De Inestrosa, Javier; Rodríguez Rodríguez, I.; Salas González, Diego; Segovia Román, Fermín; Shoeibi, Afshin
Explainable Artificial IntelligenceData scienceComputational approachesMachine learningDeep learningNeuroscienceRoboticsBiomedical applicationsComputer-aided diagnosis systems
J.M. Górriz et al. Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends. Information Fusion 100 (2023) 101945 [https://doi.org/10.1016/j.inffus.2023.101945]
SponsorshipCIBERSAM of the Instituto de Salud Carlos III 495-2020; UMA18-FEDERJA-084; Autonomous Government Andalusia (Spain) RTX A6000 48; NVIDIA Corporation 101057746; Horizon Europe project PRE-ACT; European Commission Horizon Europe Program 22 00058; Swiss State Secretariat for Education, Research and Innovation (SERI) 2020-0-01361; Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea; Artificial Intelligence Graduate School Program (Yonsei University); Funding for open access charge: Universidad de Granada / CBUA
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 humanlevel 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.