Explainable Machine Learning and Data Mining

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue on “Explainable Machine Learning and Data Mining”. This Special Issue invites researchers and industry experts to share their latest discoveries, innovative approaches, and significant progress in the field of explainability within machine learning and data mining. The goal of this Special Issue is to create a resource for discussing the current challenges, emerging trends, and state-of-the-art solutions for making machine learning and data mining models more transparent, interpretable, and accessible to a wider audience. We invite high-quality research that can drive further advancements and applications in this field.

Scope and Topics:

As machine learning and data mining models become increasingly common and complex in critical applications such as healthcare, finance, law enforcement, cybersecurity, and autonomous systems, the need for explainability and interpretability has become more important. The explainability and interpretability can ensure that these models are well understood, trusted, and ethically deployed, which is also essential for their widespread adoption and for them to be safe and responsible.

This Special Issue, entitled “Explainable Machine Learning and Data Mining”, seeks original research papers, case studies, and review articles on various aspects of explainability in machine learning and data mining.

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

• Interpretable Machine Learning Models;
• Explainability in Deep Learning;
• Visualization Techniques for Explainability;
• Post-Hoc Explanation Methods;
• Model-Agnostic Explanation Approaches;
• Causal Inference and Explainability;
• Explainability in Natural Language Processing;
• Ethical Implications of Explainable AI;
• Evaluation Metrics for Explainability;
• Human-Centric Explanations;
• Explainable Reinforcement Learning;
• Adversarial Attacks and Explainability;
• Fairness, Accountability, and Transparency in AI;
• Explainability in Autonomous Systems;
• Integration of Explainability in Industrial Applications;
• Explainability in Healthcare, Finance, and Legal AI Systems;
• Explainable AI for Edge Computing;
• Explainable AI in Robotics;
• Security and Privacy of Interpretable Machine Learning Models.

Dr. Salaheddin Hosseinzadeh
Prof. Dr. Naeem Ramzan
Dr. Nsikak Owoh
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.

• explainable AI
• interpretable machine learning
• model transparency
• ethical AI
• data mining
• post-hoc explanation
• fairness and accountability in AI
• human–AI interaction