The rapid evolution of intelligent systems in smart mobility presents unique challenges and opportunities for the research and application of explainable AI (XAI). This special track invites original contributions that explore the development, application, and evaluation of XAI methodologies tailored to the complexities of smart mobility. Topics of interest include explainable models for autonomous vehicles, interpretable decision-making in real-time traffic management, transparent algorithms for predictive maintenance in transportation systems, and beyond. Submissions are encouraged to address specific technical challenges, such as balancing model performance with transparency, ensuring accountability in safety-critical applications, and fostering trust among diverse stakeholders, from engineers to end users. Papers that propose novel frameworks, present case studies, or delve into the ethical and societal implications of explainability in smart mobility are particularly welcome. Contributions are not limited to these topics, as we aim to encourage a broad exploration of ideas and approaches relevant to the intersection of XAI and smart mobility. This track aspires to create a platform for innovative discussions, promoting advancements in XAI that can be effectively integrated into intelligent mobility solutions. Researchers and practitioners are invited to share their findings, address the unique challenges of this domain, and contribute to shaping the future of explainable AI in transportation systems.
- Explainable AI models for autonomous decision-making in smart mobility applications.
- XAI techniques for improving trust and safety in healthcare decision support systems.
- XAI frameworks for analyzing risks in autonomous vehicle navigation and healthcare procedures.
- Explainable methods for identifying and mitigating biases in smart mobility datasets.
- Out-of-distribution detection techniques using XAI for safer autonomous operations.
- XAI-driven approaches for enhancing data augmentation in healthcare and mobility contexts.
- Transparent algorithms for autonomous actuations in smart mobility systems.
- Explainable AI for improving decision-making in robotic surgeries and healthcare automation.
- Formal logic-based methods for explainability in safety-critical AI applications.
- Certification frameworks for standardizing XAI in autonomous vehicles and healthcare technologies.
- Market analysis of XAI technologies for deployment in mobility and healthcare industries.
- Business opportunities for XAI in predictive analytics for transportation and patient care.
- Interpretable AI systems for analyzing and addressing risks in healthcare treatments.
- Transparent machine learning models for accident prevention in smart mobility.
- Certification of XAI tools for regulatory compliance in safety-critical domains.
- Formal verification techniques for explainable autonomous actuations in healthcare robotics.
- XAI applications for ensuring ethical AI in smart mobility and healthcare.
- Transparent AI algorithms for improving emergency response in healthcare and transportation.
- Data-driven explainability approaches for bias mitigation in mobility and healthcare systems.
- Business strategies for leveraging XAI in safety-critical and high-value applications.
