DeepXplain 2025: Explainable Deep Neural Networks for Responsible AI

DeepXplain 2025 is a special session at IJCNN 2025 focused on improving DNN interpretability. Submit papers by Jan 15, 2025; notifications on Mar 15, 2025. Organized by Francielle Vargas, Roseli Romero, Jackson Trager, and Edson Prestes.

DeepXplain 2025 is a special session at IJCNN 2025 focusing on improving the interpretability of Deep Neural Networks (DNNs) while maintaining high predictive accuracy. This session aims to foster interdisciplinary collaboration, promote ethical AI design, and encourage the development of benchmarks and datasets for explainability research. Topics include theoretical advancements, inherently interpretable architectures, large language models, application-driven insights, and ethical evaluations.

Submit your academic papers (long or short) by January 15, 2025, in English and adhering to IJCNN-2025 formatting guidelines. Notification date is March 15, 2025, and camera-ready submissions are due May 1, 2025. Organizers include Francielle Vargas, Roseli Romero, Jackson Trager, and Edson Prestes.

Tags: DeepXplain 2025, IJCNN 2025, Deep Neural Networks, Explainable AI, Interpretability, Responsible AI, Benchmarks, Datasets, Theoretical Advancements, Inherently Interpretable Architectures, Large Language Models, Ethical Evaluations