Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends Gorriz 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 Intelligence Data science Computational approaches Machine learning Deep learning Neuroscience Robotics Biomedical applications Computer-aided diagnosis systems 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. 2023-10-27T07:57:10Z 2023-10-27T07:57:10Z 2023-07-23 journal article 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] https://hdl.handle.net/10481/85293 10.1016/j.inffus.2023.101945 eng info:eu-repo/grantAgreement/EC/Horizon Europe/22 00058 http://creativecommons.org/licenses/by-nc/4.0/ open access Atribución-NoComercial 4.0 Internacional Elsevier