Supplementary material will be provided in the virtual platform of the master to facilitate following each unit in this subject. Given the heterogeneity of topics to be dealt within this subject, with each class, references to bibliographic resources as well as other types of content (reports, multimedia, etc.) will be provided to student for the more specific aspects of the subject. The following references are of a complementary type, they deal with general aspects related to explainable and trustworthy AI.

1. V. Dignum. Responsible Artificial Intelligence. How to Developand Use AI in a Responsible Way. Springer Nature Switzerland AG, 2019, ISBN: 978-3-030-30370-9 , https://doi.org/10.1007/978-3-030-30371-6

2. A. Barredo Arrieta et al., Explainable Artificial Intelligence(XAI): Concepts, taxonomies, opportunities and challenges toward responsibleAI, Information Fusion, 58:82-115, Elsevier 2020, https://doi.org/10.1016/j.inffus.2019.12.012

3. T. Miller, Explanation in artificial intelligence: Insights fromthe social sciences. Artificial Intelligence, 267:1-38, Elsevier 2019, https://doi.org/10.1016/j.artint.2018.07.007

4. G. Vilone, L. Longo, Notions of explainability and evaluationapproaches for Explainable Artificial Intelligence, Information Fusion,76:89-106, Elsevier 2021, https://doi.org/10.1016/j.inffus.2021.05.009

5. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti,D. Pedreschi, A Survey of Methods for Explaining Black Box Models, ACMComputing Surveys, 51(5):1–42, 2019, https://dl.acm.org/doi/10.1145/3236009

6. J.M. Alonso, C. Castiello, L. Magdalena, C.Mencar, Explainable Fuzzy Systems. Paving the way from interpretable fuzzysystems to explainable AI systems. SpringerInternational Publishing, 2021, ISBN: 978-3-030-71098-9, https://doi.org/10.1007/978-3-030-71098-9