Special Issue "Advances in Explainable Artificial Intelligence (XAI)"
Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 44539
Interests: ainable artificial intelligence; defeasible argumentation; deep learning; human-centred design; mental workload modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Longo
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1400 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.
- explainable artificial intelligence (XAI)
- neuro-symbolic reasoning for XAI
- interpretable deep learning
- argument-based models of explanations
- graph neural networks for explainability
- machine learning and knowledge-graphs
- human-centric explainable AI
- interpretation of black-box models
- human-understandable machine learning
- counterfactual explanations for machine learning
- natural language processing in XAI
- quantitative/qualitative evaluation metrics for XAI
- ante and post-hoc XAI methods
- rule-based systems for XAI
- fuzzy systems and explainability
- human-centered learning and explanations
- model-dependent and model-agnostic explainability
- case-based explanations for AI systems
- interactive machine learning and explanations