Explainable Artificial Intelligence (XAI) is an emergent research field that aims to make AI and deep models’ outcomes more human-interpretable without scarifying performance. It holds the potential to increase public acceptance and trust in systems of a safety-critical nature such as Intelligent Transportation Systems (ITS). This book focuses on XAI in the field of ITS, it aims at compiling into a coherent structure the stat-of-the-art research and development of Explainable and Trustful ITS. We are seeking chapters that propose approaches that use interpretable methods to improve the interpretability of ITS applications. Chapters addressing ethical and societal implications of XAI in ITS are also solicited.
The recommended topics include, but are not limited to the following:

-Explainable models for Autonomous driving systems
-Explainable models for Traffic management and prediction
-Explainable models for Behavioral modeling and interpretation
-Explainable models for Security, privacy and safety systems
-Explainable models for air, road, and rail traffic management
-Interpretable DNN for ITS applications
-Interpretable deep reinforcement learning, inverse reinforcement learning for ITS applications
-Inherently interpretable models by design for ITS applications
-Model -agnostic methods for ITS applications such as: SHapley Additive exPlanations (SHAP), Knowledge Graphs,
LocalInterpretable Model-Agnostic Explanations (LIME), Fuzzy logic systems…. etc.
-Safety, trust, and social acceptance enabled by interpretability
-Liability, Fairness and algorithmic accountability
-Explainability and Moral dilemma
-Explainability and Standards, laws, and regulations

Submit your papers at: https://easychair.org/conferences/?conf=xa2its
For more details, kindly refer to the book project website: https://sites.google.com/usmba.ac.ma/xa2its/
For any queries, please contact: afaf.bouhoute@usmba.ac.ma

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