Privacy Expectations for Human-Autonomous Vehicle Interactions

Abstract

Robots operating in public spaces, such as autonomous vehicles, will necessarily collect images and other data concerning the people and vehicles in their vicinity, raising privacy concerns. Common conceptions of privacy in robotics do not include the challenges of many-to-many surveillance where fleets of many individual robots collect data on many people during operation. Technologists, legal scholars, and privacy researchers recommend such technologies fulfill the reasonable privacy expectations of society, but there is no standard method for measuring privacy expectations. We propose a method informed by Contextual Integrity Theory for identifying societal privacy expectations for autonomous vehicle-collected data and codifying the contextual expectations as norms. We present a study (n = 600) that identifies twelve distinct norms, which are made up of contextual factors such as the subject of data collection and the data use. In a model for tolerance of autonomous vehicle data collection, we find that both contextual factors related to the data processing and factors related to the individual are significant predictors.