Human-Centric Ridesharing on Large Scale by Explaining AI-Generated Assignments

Ridesharing has the potential to contribute to reducing CO2 emissions and congestions but user acceptance is still low. Within ridesharing platforms, the assignment of ride offers to requests and vice versa is an essential process with respect to user satisfaction and user acceptance. Our research aims to contribute to increasing ridesharing usage by improving this process. In particular, we strive to enhance existing assignment approaches that are supported by multiple AI-based techniques like deep reinforcement learning by including additional factors that influence the satisfaction of users. Furthermore, we present the novel concept of applying explainable AI techniques to make decisions in ridesharing - like assignments - more transparent to users.