Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
J. M. Górriz, Ignacio Álvarez, Agustín Álvarez-Marquina, Juan Eloy Arco, Martin Atzmueller, Fabricio Ballarini, Emilia Ivanova Barakova, Guido Bologna, P. Bonomini, German Castellanos-Dominguez, Diego Castillo-Barnés, S.B. Cho, Ricardo B. Contreras, Jorge Cuadra, Enrique Domínguez, Francisco Domínguez, Richard José Duro, David A. Elizondo, Antonio Fernández‐Caballero, E. Fernandez-Jover · Information Fusion · 2023
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 9 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.