In this chapter, we demonstrate how a design ethnographic approach to future algorithm-powered mobility solutions opens up possibilities to research social implications of automated decision making (ADM) from a situational perspective, by investigating the context of ADM rather than simply observing the technology itself and how it is used. The context of our discussion is one where the development of autonomous vehicles and artificial intelligence (AI) applications, in the service of transportation, has sparked a renewed research interest into shared mobility systems, and how these can respond to emerging challenges of rising traffic congestion and pollution levels. Our research addresses the gap between algorithm-based approaches to designing for optimizing, streamlining, and efficiency, the questions of how these systems and services are activated in people’s everyday life, and how they interfere with decision-making around traveling and shared mobility. We argue that to understand how these services and technologies will be adopted and implemented in society, research attention must be directed to people in real-life situations where this type of ADM operates.