Special Session on
Causal and Trustworthy Explainable AI for Next-Generation Intelligent Systems

Aim and Scope

The rapid deployment of Artificial Intelligence (AI) in critical domains such as healthcare, finance, cybersecurity, and autonomous systems has created an urgent need for models that are not only accurate but also interpretable, reliable, and trustworthy. Traditional Explainable AI (XAI) methods primarily focus on post-hoc explanations, which often fail to capture the underlying causal relationships in data. This special session aims to explore next-generation Explainable AI approaches by integrating causal reasoning, robustness, and trustworthiness into computational intelligence systems. The session will focus on developing AI models that provide transparent, causally grounded, and human-understandable explanations, ensuring better decision-making in high-stakes environments.

The session will bring together researchers and practitioners to discuss innovative methodologies, tools, and real-world applications that enhance the interpretability, fairness, accountability, and robustness of AI systems.

Topics of interest include, but are not limited to:

• Causal Explainable AI (Causal XAI)
• Counterfactual explanations and causal inference
• Explainability in deep learning and large language models (LLMs)
• Trustworthy AI: fairness, accountability, and transparency
• Bias detection and mitigation in AI models
• Adversarial robustness and interpretable AI systems
• Explainable reinforcement learning
• Human-centered and interactive explainability
• Multimodal and federated explainable AI
• Privacy-preserving and secure AI explanations

Paper Submission

Potential authors may submit their manuscripts for presentation consideration through ICETCI 2026 submission system electronically at https://edas.info/N34670 , following the conference guidelines. All submissions will go through peer review process. To submit your paper to this special session, you have to choose our special session title on the submission page.

Important Dates

Organizers

Dr. S. Deepika is currently working as an Assistant Professor in the Department of Computer Science and Engineering at Anurag University, Hyderabad. She has over 12 years of experience in teaching and research. Her research interests include Artificial Intelligence, Machine Learning, Explainable AI, and Trustworthy AI. She has published several research papers in reputed international journals and conferences. Her work focuses on model interpretability, fairness in AI, and data-driven intelligent systems. She has also contributed to organizing special sessions and academic events in international conferences.

Email: deepikajaiswal9963@gmail.com