Explainable AI methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS.
Amina Adadi is an assistant professor of Computer Science at Moulay Ismail University, Morocco. She has published several papers including refereed IEEE/Springer/Elsevier journal articles, conference papers, and book chapters. She has served and continues to serve on executive and technical program committees of numerous international conferences such as IEEE IRASET, ESETI, and WITS. Her research interests include Explainable AI, Data Efficient Models: Data Augmentation, Few-shot learning, Self-supervised learning, Transfer Learning, Blockchain, and Smart Contracts.
Afaf Bouhoute holds a Ph.D., a Master's degree in information systems, networking, and multimedia, and a bachelor's degree in computer science, all from the faculty of science, Sidi Mohamed Ben Abdellah University, Fez, Morocco. She regularly serves in the technical and program committees of numerous international conferences such as ISCV, WINCOM, ICECOCS, and ICDS. She also served as a co-chair of the First International Workshop on Cooperative Vehicle Networking (CVNET 2020), which was organized in conjunction with EAI ADHOCNETS 2020. Her research interests span different techniques and algorithms for modeling and analysis of driving behavior, with a focus on their application in cooperative intelligent transportation systems.