Formal Methods in the Loop: Trustworthy AI Pipelines
A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).
Deadline for manuscript submissions: 28 February 2027 | Viewed by 102
Editors
Interests: stochastic models; SAT design patterns; artificial intelligence
2. Faculty of Informatics, Eszterházy Károly Catholic University, 3300 Eger, Hungary
Interests: formal languages; automata; digital geometry; digital image processing; new computing paradigms; DNA computing; artificial intelligence; logical systems; puzzles; algorithms; graphs; parallelism; bio-inspired computing; soft computing; theoretical computer science; discrete mathematics; combinatorics, cognitive sciences
Special Issue Information
Dear Colleagues,
Large language model-based systems have demonstrated remarkable capabilities in a wide range of applications, from conversational agents and code assistants to multimodal content generation. A new generation is emerging, marked by agent-oriented AI, context-aware AI, and AI with reasoning. But most of them still operate as black boxes. This raises a fundamental question: can we trust the answers produced by AI systems?
Explainable artificial intelligence (XAI) addresses the fundamental goal of making AI systems understandable and trustworthy for human users. In this context, formal methods provide mathematically grounded tools—such as specification, verification, and reasoning—which can support this goal by enabling explanations with formal guarantees of correctness and reliability.
This Special Issue provides a forum for advancing these ideas and fostering dialogue between formal methods, theoretical computer science, and AI research communities.
Topics of interest include, but are not limited to, the following:
- Formal methods and mathematical foundations of AI;
- Explainable, verifiable, and trustworthy AI;
- AI using formal methods;
- Context-aware and/or agent-based systems with formal guarantees;
- Principled and structured vibe coding approaches;
- AI ethics, accountability, and fairness;
- Critical thinking using AI, and critical use of AI;
- Human-in-the-loop AI with control frameworks;
- Digital twin technologies with formal foundations.
Dr. Gábor Kusper
Dr. Benedek Nagy
Dr. Gergely Kovásznai
Guest Editors
Manuscript Submission Information
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Keywords
- explainable artificial intelligence
- mathematical foundations of AI
- trustworthy AI
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