Explainability in AI and Machine Learning

Dear Colleagues,

Explainable Artificial Intelligence (XAI) in general concerns the problem of communicating explanations to human users by AI systems regarding their decisions. This has been of natural interest in systems or models of "traditional" AI (e.g., knowledge representation and reasoning systems, planning systems), where the "internal" decision-making process is mostly transparent (white-box models), which, although mostly interpretable, are not explainable. However, recently, due to the development of many successful models, explainability is of particular concern to the machine learning (ML) community. This is because, although some models are interpretable, most ML models act like black-boxes, and in many applications (e.g., medicine, healthcare, education, automated driving), practitioners want to understand models' decision making, to be able to trust them when used in reality.

So, XAI has become an active subfield of machine learning aiming at increasing the transparency of machine learning models. Explainability, apart from increasing trust and confidence, can also provide further insights regarding the model itself and the problem.

Deep Neural Networks (DNNs) are ML models that have achieved major advances. However, a clear understanding of their internal decision making is lacking. Interpreting the internal mechanisms of DNNs has been a very interesting topic. Symbolic methods could be used for network interpretation, by making clear inference patterns inside DNNs, and explaining the decisions made by them. On the other hand, re-designing DNNs in an interpretable or explainable way could be a solution.

Natural language (NL) techniques, such NL Generation (NLG) and NL Processing (NLP), can help in providing comprehensible explanations of automated decisions to human users of AI systems.

Topics of interest include, but are not limited to, the following:

• Applications of XAI systems;
• Evaluation of XAI approaches;
• Explainable Agents;
• Explaining Black-box Models;
• Explaining Logical Formulas;
• Explainable Machine Learning;
• Explainable Planning;
• Interpretable Machine Learning;
• Metrics for Explainability Evaluation;
• Models for Explainable Recommendations;
• Natural Language Generation for Explainable AI;
• Self-explanatory Decision-Support Systems;
• Verbalizing Knowledge Bases.

Prof. Dr. Ioannis Hatzilygeroudis
Prof. Dr. Vasile Palade
Dr. Isidoros Perikos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.