The field of urban transportation and AI systems is moving towards a more sustainable and equitable future. Researchers are focusing on optimizing charging infrastructure for electric vehicles, ensuring that the transition to low-emission transportation systems is efficient and accessible to all. Additionally, there is a growing emphasis on fairness and bias mitigation in AI systems, particularly in applications such as mobility demand forecasting, point-of-interest recommendation, and fault localization in deep neural networks. Noteworthy papers include: A United Framework for Planning Electric Vehicle Charging Accessibility, which proposes a scalable methodology for incorporating equity considerations into EV infrastructure planning. FairDRL-ST, which achieves fairness in spatio-temporal prediction without compromising performance. Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation, which effectively captures spatial-temporal transition representations of POIs. FairFLRep, which identifies and corrects potentially bias-inducing neurons in DNN classifiers. Who pays the RENT, which informs the deployment of AI-based solutions in social service provision that account for particular applications and geographies.