In the era of the Internet of Things and Big Data, data scientists are required to extract valuable knowledge from the given data. They first analyze, cure and pre-process data. Then, they apply Artificial Intelligence (AI) techniques to automatically extract knowledge from data.

Our focus is on knowledge representation and how to enhance human-machine interaction. As remarked in the last challenge stated by the USA Defense Advanced Research Projects Agency (DARPA), "even though current AI systems offer many benefits in many applications, their effectiveness is limited by a lack of explanation ability when interacting with humans". Accordingly, users without a strong background on AI, require a new generation of explainable AI systems (XAI in short). They are expected to naturally interact with humans, thus providing comprehensible explanations of decisions automatically made. Thus, the goal of this special session is to discuss and disseminate the most recent advancements focused on explainable AI. The session goes a step ahead with respect to the previous events we organized in some other conferences: XAI@INLG2019, XAI@FUZZ-IEEE2019, XAI@IEEESMC, IPMU2018, FUZZ-IEEE2017, IFSA-EUSFLAT2015, EUSFLAT 2013, IEEEWCCI 2012, IEEEWCCI 2010, and joint IFSA-EUSFLAT 2009. Moreover, this session is supported by the H2020 MSCA-ITN-2019 NL4XAI project.

The aim of this session is to offer an opportunity for researchers and practitioners to identify new promising research directions on XAI and to provide a forum to disseminate and discuss XAI, with special attention to Interpretable Fuzzy Systems. Notice that about 30% of publications in Scopus (20 October 2017) regarding XAI came from authors well recognized in the field of Fuzzy Logic, as described in the following publication:

Jose M. Alonso, C. Castiello, C. Mencar, "A Bibliometric Analysis of the Explainable Artificial Intelligence Research Field", 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Cádiz, Spain, Springer, CCIS 853:2-15, 2018.