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

2026-02-11

AI Summary

This study addresses the widespread user complacency toward continuous in-vehicle data monitoring in autonomous driving contexts, where privacy risks are often overlooked. Through semi-structured interviews with 16 participants and grounded theory analysis from a constructivist perspective, the research reveals how users perceive vehicular surveillance as a natural extension of digital platform monitoring. It introduces two core concepts: the “surveillance ecology” and the “datafied car as a permeable, mobile extension of the home.” The work identifies a “third-layer digital divide” stemming from geographically constrained data access and critiques the structural reliance of machine learning on data-intensive paradigms. To counter these issues, the paper advocates for universal data access rights, mandatory transparency, and data minimization standards to guide industry governance, foster societal learning, and curb the escalating datafication race.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• autonomous vehicles
• surveillance
• privacy trade-offs
• datafication
• platform governance

Innovation

• surveillance ecology
• datafication
• tertiary digital divides
• machine learning
• platform governance