Symmetry and Asymmetry in Machine Learning for Trustworthy Applications
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 303
Special Issue Editor
Special Issue Information
Dear Colleagues,
Symmetry and asymmetry are fundamental principles underlying mathematics, physics, information theory, and computational sciences. In recent years, these concepts have become increasingly central to machine learning (ML). Symmetry governs invariance and equivariance properties in models, data representations, and learning algorithms, while asymmetry characterizes directional relationships, hierarchical dependencies, causal structures, and bias propagation in intelligent systems.
Modern ML systems—including deep neural networks, graph learning models, generative AI, and foundation models—explicitly or implicitly exploit symmetry (e.g., permutation invariance, translation equivariance, and group invariance) to enhance generalization and efficiency. Conversely, asymmetry naturally arises in real-world data distributions, class imbalance, adversarial settings, and structured information flows.
As ML technologies are increasingly deployed in high-stakes domains such as healthcare, finance, and autonomous systems, understanding the interplay between symmetry and asymmetry is essential for developing trustworthy AI. Properties such as robustness, interpretability, fairness, and stability can be analyzed through the structural lens of symmetry-aware modeling and controlled symmetry breaking.
Aim and Scope:
This Special Issue aims to advance the theoretical foundations, methodological developments, and practical implementations of symmetry and asymmetry in machine learning for trustworthy applications. We welcome original research articles and review papers addressing theory, algorithmic design, optimization, structural analysis, and real-world implementations grounded in symmetry-aware or asymmetry-aware principles.
Topics of interest include, but are not limited to, the following:
- Machine learning algorithms and frameworks focusing on the design, optimization, and evaluation of symmetry-aware or asymmetry-aware methods for practical and trustworthy AI;
- Applied ML systems leveraging symmetry/asymmetry to improve performance, generalization, robustness, and fairness;
- Real-world data challenges, including class imbalance, distributional shifts, causal or directional structures, and asymmetric information flow;
- Trustworthy AI approaches include fairness, bias mitigation, adversarial robustness, explainability, transparent generative AI, and human-in-the-loop learning mechanisms;
- Domain applications such as healthcare analytics, smart cities, fintech, cybersecurity, autonomous systems, robotics, and decision support systems.
We look forward to receiving your contributions.
Dr. Punnarumol Temdee
Guest Editor
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. Symmetry is an international peer-reviewed open access monthly 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
- symmetry
- asymmetry
- machine learning algorithms
- generative AI
- trustworthy AI
- explainable and interpretable AI
- human-in-the-loop systems
- machine learning applications
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