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

TL;DR

This study explores how autonomous vehicle drivers perceive and rationalize surveillance, showing they normalize data collection by comparing it to other digital platforms and highlighting the need for better governance to protect privacy.

Contribution

It reveals that AV users view surveillance as normalized within their digital ecosystem and proposes governance measures to address privacy concerns and data asymmetries.

Findings

Drivers show minimal AV-specific privacy concerns.

AV surveillance is normalized through comparisons with digital platforms.

Data access restrictions create privacy knowledge gaps.

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.