Call for Papers: Special Issue on Trustworthy Neurosymbolic AI in Regulated Domains: Advances, Challenges, and Applications

Neurosymbolic AI is an emerging field that combines the strengths of neural networks and symbolic reasoning to create AI systems that are both powerful and interpretable. This hybrid approach addresses the limitations of traditional AI systems by providing robust, interpretable, and efficient solutions to complex problems. In highly regulated and mission-critical domains, such as healthcare, law and finance, compliance with regulations and standards is crucial. Neurosymbolic AI can help bridge the gap between intelligence and compliance by offering more transparent and explainable AI systems. By requiring alignment of neural network behaviour with compliance requirements specified as rules, regulations, guidelines, and policies encoded in symbolic representation, these systems can provide clear reasoning and justifications for their decisions, making it easier to ensure compliance with regulatory requirements. Despite its promising advancements, challenges such as scalability, integration complexity, and ensuring fairness and transparency remain. Addressing these challenges through interdisciplinary research is crucial for fully realising the potential of Neurosymbolic AI.

The goal of this special issue is to publish state-of-the-art work in Neurosymbolic AI in regulated domains, highlighting both challenges and advancements. We invite research contributions that  develop systems integrating symbolic and neural networks to meet at least some of the key criteria for regulated domains, including accuracy, explainability, fairness, safety and assurance. Additionally, we welcome original research that addresses pressing challenges such as scalability and efficiency, ensuring that these hybrid approaches perform reliably even with large datasets and complex tasks. We are also particularly interested in studies that tackle issues related to fairness, bias mitigation, and accountability.

Topics of Interest

Topics of interest include (but not limited to):

• Explainability through/for Neurosymbolic AI
• Fairness Evaluation in Neurosymbolic AI
• AI Safety through/for Neurosymbolic AI
• AI Assurance through/for Neurosymbolic AI
• Knowledge Engineering for Neurosymbolic AI
• Scalability and Efficiency of Neurosymbolic AI systems
• Regulated domain use cases.
• Human-in-loop systems.

Deadline

September 30, 2025. Paper submitted before the deadline will be processed as soon as they
are received.

Guest Editors

• Professor Dhaval Thakker, University of Hull, UK 
• Professor Amit Sheth, University of South Carolina, USA 
• Dr Bhupesh Mishra, University of Hull, UK 
• Dr Koorosh Aslansefat, University of Hull, UK 
• Dr Tejal Shah, Newcastle University, UK 

Contact email for the guest editors: nesy-regulated@googlegroups.com

Guest Editorial Board

• TBC

Author Guidelines

We invite full papers, dataset descriptions, application reports and reports on tools and systems. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this special issue. Authors can extend previously published conference or workshop papers - see the submission guidelines at https://neurosymbolic-ai-journal.com/content/author-guidelines for details.

Submissions shall be made through the journal website at https://neurosymbolic-ai-journal.com/. Prospective authors must take notice of the submission guidelines posted at https://neurosymbolic-ai-journal.com/content/author-guidelines.

Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Trustworthy Neurosymbolic AI in Regulated Domains: Advances, Challenges, and Applications. All manuscripts will be reviewed based on the journal's open and transparent review policy and will be made available online during the review process.