Normalized Surveillance in the Datafied Car: How Autonomous Vehicle Users Rationalize Privacy Trade-offs

TL;DR

This study investigates how autonomous vehicle drivers perceive and rationalize pervasive vehicular surveillance amid broad datafication. Using 16 in-depth interviews and constructivist grounded theory, it shows minimal AV-specific privacy concerns and reveals three frames—trust in platform security, technological necessity, and platform equivalence—that explain rationalization of data collection. The work situates AV surveillance within the broader surveillance ecology and identifies geographic data-access asymmetries that hinder informed deliberation, illustrating a shift from the privacy paradox to digital resignation. It argues for governance and design interventions (data access rights, minimization, transparency, and visible data flows) to democratize social learning and curb race-to-the-bottom data extraction, highlighting the need to address underlying business models that incentivize pervasive surveillance.

Abstract

Autonomous vehicles (AVs) are characterized by pervasive datafication and surveillance through sensors like in-cabin cameras, LIDAR, and GPS. Drawing on 16 semi-structured interviews with AV drivers analyzed using constructivist grounded theory, this study examines how users make sense of vehicular surveillance within everyday datafication. Findings reveal drivers demonstrate few AV-specific privacy concerns, instead normalizing monitoring through comparisons with established digital platforms. We theorize this indifference by situating AV surveillance within the `surveillance ecology' of platform environments, arguing the datafied car functions as a mobile extension of the `leaky home' -- private spaces rendered permeable through connected technologies continuously transmitting behavioral data. The study contributes to scholarship on surveillance beliefs, datafication, and platform governance by demonstrating how users who have accepted comprehensive smartphone and smart home monitoring encounter AV datafication as just another node in normalized data extraction. We highlight how geographic restrictions on data access -- currently limiting driver log access to California -- create asymmetries that impede informed privacy deliberation, exemplifying `tertiary digital divides.' Finally, we examine how machine learning's reliance on data-intensive approaches creates structural pressure for surveillance that transcends individual manufacturer choices. We propose governance interventions to democratize social learning, including universal data access rights, binding transparency requirements, and data minimization standards to prevent race-to-the-bottom dynamics in automotive datafication.