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Electronics
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The Explainable AI Revolution: From Black Boxes to Transparent Insights
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The Explainable AI Revolution: From Black Boxes to Transparent Insights

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 March 2026 | Viewed by 754

Special Issue Editors

Dr. Raheleh Jafari

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Guest Editor
School of Design, University of Leeds, Leeds LS2 9JT, UK
Interests: the development of artificial intelligence methods and their application in robotics, automation and fashion technology
Special Issues, Collections and Topics in MDPI journals
Dr. Alexander Gegov

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Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: artificial intelligence; machine learning methods using fuzzy systems; neural networks and evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Farzad Arabikhan

E-Mail Website
Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: fuzzy logic; artificial intelligence; machine learning; AI and ML applications; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Dr. Alexandar Ichtev

E-Mail Website
Guest Editor
Department of Systems and Control, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: control systems; fuzzy control; fault tolerant control

Special Issue Information

Dear Colleagues,

This Special Issue invites researchers to contribute original research exploring the burgeoning field of explainable artificial intelligence (XAI). As AI models become increasingly complex and integrated into critical applications, the demand for transparency, interpretability, and trustworthiness has never been more pressing. This Special Issue aims to gather cutting-edge advancements in XAI methodologies, applications, and their societal implications. We welcome submissions that delve into novel techniques for interpreting diverse AI models, evaluate the effectiveness of XAI in real-world scenarios, and address the ethical, legal, and social challenges associated with deploying transparent AI systems.

We are particularly interested in papers that bridge the gap between theoretical XAI concepts and practical implementations across various domains, including healthcare, finance, autonomous systems, and scientific discovery. Topics of interest include, but are not limited to, post hoc explanation methods, intrinsically interpretable models, fuzzy logic-based XAI, explainable fuzzy neural networks, causality in XAI, human–AI collaboration for interpretability, user studies on XAI effectiveness, and the role of XAI in ensuring fairness and accountability in AI decision making. Through this Special Issue, we seek to foster a deeper understanding of how XAI can transform AI from an opaque “black box” into a powerful, transparent, and trusted tool that empowers user and promotes responsible innovation.

We look forward to receiving your valuable contributions.

Dr. Raheleh Jafari
Dr. Alexander Gegov
Dr. Farzad Arabikhan
Dr. Alexandar Ichtev
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • explainable AI
  • interpretability
  • fuzzy systems
  • transparency
  • accountability

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Published Papers (1 paper)

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Research

18 pages, 406 KB  
Article
Explainable AI for Federated Learning-Based Intrusion Detection Systems in Connected Vehicles
by Ramin Taheri, Raheleh Jafari, Alexander Gegov, Farzad Arabikhan and Alexandar Ichtev
Electronics 2025, 14(22), 4508; https://doi.org/10.3390/electronics14224508 - 18 Nov 2025
Viewed by 467
Abstract
Connected and autonomous vehicles, along with the expanding Internet of Vehicles (IoV), are increasingly exposed to complex and evolving cyberattacks. Consequently, Intrusion Detection Systems (IDS) have become a vital component of modern vehicular cybersecurity. Federated Learning (FL) enables multiple vehicles to collaboratively train [...] Read more.
Connected and autonomous vehicles, along with the expanding Internet of Vehicles (IoV), are increasingly exposed to complex and evolving cyberattacks. Consequently, Intrusion Detection Systems (IDS) have become a vital component of modern vehicular cybersecurity. Federated Learning (FL) enables multiple vehicles to collaboratively train detection models while keeping their local data private, providing a decentralized alternative to traditional centralized learning. Despite these advantages, FL-based IDS frameworks remain vulnerable to attacks. To address this vulnerability, we propose an explainable federated intrusion detection framework that enhances both the security and interpretability of IDS in connected vehicles. The framework employs a Deep Neural Network (DNN) within a federated setting and integrates explainability through the Shapley Additive Explanations (SHAP) method. This Explainable Artificial Intelligence (XAI) component identifies the most influential network features contributing to detection decisions and assists in recognizing anomalies arising from malicious or corrupted clients. Experimental validation on the CICEVSE2024 and CICIoV2024 vehicular datasets demonstrates that the proposed system achieves high detection accuracy. Moreover, the XAI module improves transparency and enables analysts to verify and understand the model’s decision-making process. Compared with both centralized IDS models and conventional federated approaches without explainability, the proposed system delivers comparable performance, stronger resilience to attacks, and significantly enhanced interpretability. Overall, this work demonstrates that integrating FL with XAI provides a privacy-preserving and trustworthy approach for intrusion detection in connected vehicular networks. Full article
(This article belongs to the Special Issue The Explainable AI Revolution: From Black Boxes to Transparent Insights)
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